#%tensorflow_version 2.x
import tensorflow as tf
tf.__version__
'2.7.0'
import numpy as np
import pandas as pd
import seaborn as sns
import scipy.stats as stats
import matplotlib.pyplot as plt
from tensorflow import keras
%matplotlib inline
#Test Train Split
from sklearn.model_selection import train_test_split
#Feature Scaling library
from sklearn.preprocessing import StandardScaler
import pickle
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Flatten, Dense
from tensorflow.keras import regularizers, optimizers
from sklearn.metrics import r2_score
from tensorflow.keras.models import load_model
# Initialize the random number generator
import random
seed = 7
np.random.seed(seed)
# Ignore the warnings
import warnings
warnings.filterwarnings("ignore")
#Read the data as a data frame
mydata = pd.read_csv(r"Signal.xls")
mydata.head(20)
| Parameter 1 | Parameter 2 | Parameter 3 | Parameter 4 | Parameter 5 | Parameter 6 | Parameter 7 | Parameter 8 | Parameter 9 | Parameter 10 | Parameter 11 | Signal_Strength | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7.4 | 0.700 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 |
| 1 | 7.8 | 0.880 | 0.00 | 2.6 | 0.098 | 25.0 | 67.0 | 0.9968 | 3.20 | 0.68 | 9.8 | 5 |
| 2 | 7.8 | 0.760 | 0.04 | 2.3 | 0.092 | 15.0 | 54.0 | 0.9970 | 3.26 | 0.65 | 9.8 | 5 |
| 3 | 11.2 | 0.280 | 0.56 | 1.9 | 0.075 | 17.0 | 60.0 | 0.9980 | 3.16 | 0.58 | 9.8 | 6 |
| 4 | 7.4 | 0.700 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 |
| 5 | 7.4 | 0.660 | 0.00 | 1.8 | 0.075 | 13.0 | 40.0 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 |
| 6 | 7.9 | 0.600 | 0.06 | 1.6 | 0.069 | 15.0 | 59.0 | 0.9964 | 3.30 | 0.46 | 9.4 | 5 |
| 7 | 7.3 | 0.650 | 0.00 | 1.2 | 0.065 | 15.0 | 21.0 | 0.9946 | 3.39 | 0.47 | 10.0 | 7 |
| 8 | 7.8 | 0.580 | 0.02 | 2.0 | 0.073 | 9.0 | 18.0 | 0.9968 | 3.36 | 0.57 | 9.5 | 7 |
| 9 | 7.5 | 0.500 | 0.36 | 6.1 | 0.071 | 17.0 | 102.0 | 0.9978 | 3.35 | 0.80 | 10.5 | 5 |
| 10 | 6.7 | 0.580 | 0.08 | 1.8 | 0.097 | 15.0 | 65.0 | 0.9959 | 3.28 | 0.54 | 9.2 | 5 |
| 11 | 7.5 | 0.500 | 0.36 | 6.1 | 0.071 | 17.0 | 102.0 | 0.9978 | 3.35 | 0.80 | 10.5 | 5 |
| 12 | 5.6 | 0.615 | 0.00 | 1.6 | 0.089 | 16.0 | 59.0 | 0.9943 | 3.58 | 0.52 | 9.9 | 5 |
| 13 | 7.8 | 0.610 | 0.29 | 1.6 | 0.114 | 9.0 | 29.0 | 0.9974 | 3.26 | 1.56 | 9.1 | 5 |
| 14 | 8.9 | 0.620 | 0.18 | 3.8 | 0.176 | 52.0 | 145.0 | 0.9986 | 3.16 | 0.88 | 9.2 | 5 |
| 15 | 8.9 | 0.620 | 0.19 | 3.9 | 0.170 | 51.0 | 148.0 | 0.9986 | 3.17 | 0.93 | 9.2 | 5 |
| 16 | 8.5 | 0.280 | 0.56 | 1.8 | 0.092 | 35.0 | 103.0 | 0.9969 | 3.30 | 0.75 | 10.5 | 7 |
| 17 | 8.1 | 0.560 | 0.28 | 1.7 | 0.368 | 16.0 | 56.0 | 0.9968 | 3.11 | 1.28 | 9.3 | 5 |
| 18 | 7.4 | 0.590 | 0.08 | 4.4 | 0.086 | 6.0 | 29.0 | 0.9974 | 3.38 | 0.50 | 9.0 | 4 |
| 19 | 7.9 | 0.320 | 0.51 | 1.8 | 0.341 | 17.0 | 56.0 | 0.9969 | 3.04 | 1.08 | 9.2 | 6 |
mydata['Signal_Strength'].unique()
array([5, 6, 7, 4, 8, 3], dtype=int64)
mydata.shape
(1599, 12)
mydata.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1599 entries, 0 to 1598 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Parameter 1 1599 non-null float64 1 Parameter 2 1599 non-null float64 2 Parameter 3 1599 non-null float64 3 Parameter 4 1599 non-null float64 4 Parameter 5 1599 non-null float64 5 Parameter 6 1599 non-null float64 6 Parameter 7 1599 non-null float64 7 Parameter 8 1599 non-null float64 8 Parameter 9 1599 non-null float64 9 Parameter 10 1599 non-null float64 10 Parameter 11 1599 non-null float64 11 Signal_Strength 1599 non-null int64 dtypes: float64(11), int64(1) memory usage: 150.0 KB
All the parameter values are in float except the Signal strength
# Checking the presence of missing values
null_counts = mydata.isnull().sum() # This prints the columns with the number of null values they have
print (null_counts)
Parameter 1 0 Parameter 2 0 Parameter 3 0 Parameter 4 0 Parameter 5 0 Parameter 6 0 Parameter 7 0 Parameter 8 0 Parameter 9 0 Parameter 10 0 Parameter 11 0 Signal_Strength 0 dtype: int64
# 5 point summary of numerical attributes
mydata.describe()
| Parameter 1 | Parameter 2 | Parameter 3 | Parameter 4 | Parameter 5 | Parameter 6 | Parameter 7 | Parameter 8 | Parameter 9 | Parameter 10 | Parameter 11 | Signal_Strength | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 |
| mean | 8.319637 | 0.527821 | 0.270976 | 2.538806 | 0.087467 | 15.874922 | 46.467792 | 0.996747 | 3.311113 | 0.658149 | 10.422983 | 5.636023 |
| std | 1.741096 | 0.179060 | 0.194801 | 1.409928 | 0.047065 | 10.460157 | 32.895324 | 0.001887 | 0.154386 | 0.169507 | 1.065668 | 0.807569 |
| min | 4.600000 | 0.120000 | 0.000000 | 0.900000 | 0.012000 | 1.000000 | 6.000000 | 0.990070 | 2.740000 | 0.330000 | 8.400000 | 3.000000 |
| 25% | 7.100000 | 0.390000 | 0.090000 | 1.900000 | 0.070000 | 7.000000 | 22.000000 | 0.995600 | 3.210000 | 0.550000 | 9.500000 | 5.000000 |
| 50% | 7.900000 | 0.520000 | 0.260000 | 2.200000 | 0.079000 | 14.000000 | 38.000000 | 0.996750 | 3.310000 | 0.620000 | 10.200000 | 6.000000 |
| 75% | 9.200000 | 0.640000 | 0.420000 | 2.600000 | 0.090000 | 21.000000 | 62.000000 | 0.997835 | 3.400000 | 0.730000 | 11.100000 | 6.000000 |
| max | 15.900000 | 1.580000 | 1.000000 | 15.500000 | 0.611000 | 72.000000 | 289.000000 | 1.003690 | 4.010000 | 2.000000 | 14.900000 | 8.000000 |
Parameter 3 ranges between 0 and 1.
Maximum value of Parameter 5 is 0.6
Parameter 8 has a very low range between 0.9 and 1.004 Standard deviation is lowest for Parameter 8, it is 0.001887
# studying the distribution of continuous attributes
cols = list(mydata)
for i in np.arange(len(cols)):
sns.distplot(mydata[cols[i]], color='blue')
plt.show()
print('Distribution of ',cols[i])
print('Mean is:',mydata[cols[i]].mean())
print('Median is:',mydata[cols[i]].median())
print('Mode is:',mydata[cols[i]].mode())
print('Standard deviation is:',mydata[cols[i]].std())
print('Skewness is:',mydata[cols[i]].skew())
print('Maximum is:',mydata[cols[i]].max())
print('Minimum is:',mydata[cols[i]].min())
Distribution of Parameter 1 Mean is: 8.319637273295838 Median is: 7.9 Mode is: 0 7.2 dtype: float64 Standard deviation is: 1.7410963181277006 Skewness is: 0.9827514413284587 Maximum is: 15.9 Minimum is: 4.6
Distribution of Parameter 2 Mean is: 0.5278205128205131 Median is: 0.52 Mode is: 0 0.6 dtype: float64 Standard deviation is: 0.17905970415353498 Skewness is: 0.6715925723840199 Maximum is: 1.58 Minimum is: 0.12
Distribution of Parameter 3 Mean is: 0.2709756097560964 Median is: 0.26 Mode is: 0 0.0 dtype: float64 Standard deviation is: 0.19480113740531785 Skewness is: 0.3183372952546368 Maximum is: 1.0 Minimum is: 0.0
Distribution of Parameter 4 Mean is: 2.5388055034396517 Median is: 2.2 Mode is: 0 2.0 dtype: float64 Standard deviation is: 1.4099280595072805 Skewness is: 4.54065542590319 Maximum is: 15.5 Minimum is: 0.9
Distribution of Parameter 5 Mean is: 0.08746654158849257 Median is: 0.079 Mode is: 0 0.08 dtype: float64 Standard deviation is: 0.047065302010090154 Skewness is: 5.680346571971724 Maximum is: 0.611 Minimum is: 0.012
Distribution of Parameter 6 Mean is: 15.874921826141339 Median is: 14.0 Mode is: 0 6.0 dtype: float64 Standard deviation is: 10.46015696980973 Skewness is: 1.250567293314441 Maximum is: 72.0 Minimum is: 1.0
Distribution of Parameter 7 Mean is: 46.46779237023139 Median is: 38.0 Mode is: 0 28.0 dtype: float64 Standard deviation is: 32.89532447829901 Skewness is: 1.515531257594554 Maximum is: 289.0 Minimum is: 6.0
Distribution of Parameter 8 Mean is: 0.9967466791744831 Median is: 0.99675 Mode is: 0 0.9972 dtype: float64 Standard deviation is: 0.0018873339538425559 Skewness is: 0.07128766294927483 Maximum is: 1.00369 Minimum is: 0.99007
Distribution of Parameter 9 Mean is: 3.311113195747343 Median is: 3.31 Mode is: 0 3.3 dtype: float64 Standard deviation is: 0.15438646490354266 Skewness is: 0.19368349811284427 Maximum is: 4.01 Minimum is: 2.74
Distribution of Parameter 10 Mean is: 0.6581488430268921 Median is: 0.62 Mode is: 0 0.6 dtype: float64 Standard deviation is: 0.16950697959010977 Skewness is: 2.4286723536602945 Maximum is: 2.0 Minimum is: 0.33
Distribution of Parameter 11 Mean is: 10.422983114446502 Median is: 10.2 Mode is: 0 9.5 dtype: float64 Standard deviation is: 1.0656675818563965 Skewness is: 0.8608288069184189 Maximum is: 14.9 Minimum is: 8.4
Distribution of Signal_Strength Mean is: 5.6360225140712945 Median is: 6.0 Mode is: 0 5 dtype: int64 Standard deviation is: 0.8075694397347023 Skewness is: 0.21780157547366327 Maximum is: 8 Minimum is: 3
fig, ax = plt.subplots()
width = len(mydata['Signal_Strength'].unique()) + 4
fig.set_size_inches(width,4)
ax=sns.countplot(data = mydata, x= 'Signal_Strength')
plt.title('Distribution of Signal_Strength')
for p in ax.patches:
ax.annotate(str((np.round(p.get_height()/len(mydata)*100,decimals=2)))+'%', (p.get_x()+p.get_width()/2., p.get_height()), ha='center', va='center', xytext=(0, 5), textcoords='offset points')
#plt.figure(figsize = (50,50))
sns.pairplot(mydata,diag_kind='kde')
plt.show()
1.Parameter 6 and Parameter 7 are highly correlated with each other and visce versa and they have almost 0 correlation with other Parameters
2.Parameter 1 is positively correlated to Parameter 3 and Parameter 8 and negatively correlated to Parameter 2 and Parameter 9.
3.Parameter 4 is has very low correlation with other Parameters.
fig,ax = plt.subplots(figsize=(15, 10))
sns.heatmap(mydata.corr(), ax=ax, annot=True, linewidths=0.05, fmt= '.2f',cmap="YlGnBu")
plt.show()
Since high correlation coefficient value lies between ± 0.50 and ± 1 Parameter 1 is highly correlated with Parameter 3 and Parameter 8, Parameter 9. Parameter 6 and 7 are highly correlated. But since, the correlation is not too high near 0.8 or above not dropping the features.
# Checking the presence of outliers
l = len(mydata)
col = list(mydata.columns)
#col.remove('condition')
for i in np.arange(len(col)):
sns.boxplot(x= mydata[col[i]], color='cyan')
plt.show()
print('Boxplot of ',col[i])
#calculating the outiers in attribute
Q1 = mydata[col[i]].quantile(0.25)
Q2 = mydata[col[i]].quantile(0.50)
Q3 = mydata[col[i]].quantile(0.75)
IQR = Q3 - Q1
L_W = (Q1 - 1.5 *IQR)
U_W = (Q3 + 1.5 *IQR)
print('Q1 is : ',Q1)
print('Q2 is : ',Q2)
print('Q3 is : ',Q3)
print('IQR is:',IQR)
print('Lower Whisker, Upper Whisker : ',L_W,',',U_W)
bools = (mydata[col[i]] < (Q1 - 1.5 *IQR)) |(mydata[col[i]] > (Q3 + 1.5 * IQR))
print('Out of ',l,' rows in data, number of outliers are:',bools.sum()) #calculating the number of outliers
Boxplot of Parameter 1 Q1 is : 7.1 Q2 is : 7.9 Q3 is : 9.2 IQR is: 2.0999999999999996 Lower Whisker, Upper Whisker : 3.95 , 12.349999999999998 Out of 1599 rows in data, number of outliers are: 49
Boxplot of Parameter 2 Q1 is : 0.39 Q2 is : 0.52 Q3 is : 0.64 IQR is: 0.25 Lower Whisker, Upper Whisker : 0.015000000000000013 , 1.0150000000000001 Out of 1599 rows in data, number of outliers are: 19
Boxplot of Parameter 3 Q1 is : 0.09 Q2 is : 0.26 Q3 is : 0.42 IQR is: 0.32999999999999996 Lower Whisker, Upper Whisker : -0.4049999999999999 , 0.9149999999999999 Out of 1599 rows in data, number of outliers are: 1
Boxplot of Parameter 4 Q1 is : 1.9 Q2 is : 2.2 Q3 is : 2.6 IQR is: 0.7000000000000002 Lower Whisker, Upper Whisker : 0.8499999999999996 , 3.6500000000000004 Out of 1599 rows in data, number of outliers are: 155
Boxplot of Parameter 5 Q1 is : 0.07 Q2 is : 0.079 Q3 is : 0.09 IQR is: 0.01999999999999999 Lower Whisker, Upper Whisker : 0.04000000000000002 , 0.11999999999999998 Out of 1599 rows in data, number of outliers are: 112
Boxplot of Parameter 6 Q1 is : 7.0 Q2 is : 14.0 Q3 is : 21.0 IQR is: 14.0 Lower Whisker, Upper Whisker : -14.0 , 42.0 Out of 1599 rows in data, number of outliers are: 30
Boxplot of Parameter 7 Q1 is : 22.0 Q2 is : 38.0 Q3 is : 62.0 IQR is: 40.0 Lower Whisker, Upper Whisker : -38.0 , 122.0 Out of 1599 rows in data, number of outliers are: 55
Boxplot of Parameter 8 Q1 is : 0.9956 Q2 is : 0.99675 Q3 is : 0.997835 IQR is: 0.002234999999999987 Lower Whisker, Upper Whisker : 0.9922475000000001 , 1.0011875 Out of 1599 rows in data, number of outliers are: 45
Boxplot of Parameter 9 Q1 is : 3.21 Q2 is : 3.31 Q3 is : 3.4 IQR is: 0.18999999999999995 Lower Whisker, Upper Whisker : 2.925 , 3.6849999999999996 Out of 1599 rows in data, number of outliers are: 35
Boxplot of Parameter 10 Q1 is : 0.55 Q2 is : 0.62 Q3 is : 0.73 IQR is: 0.17999999999999994 Lower Whisker, Upper Whisker : 0.28000000000000014 , 0.9999999999999999 Out of 1599 rows in data, number of outliers are: 59
Boxplot of Parameter 11 Q1 is : 9.5 Q2 is : 10.2 Q3 is : 11.1 IQR is: 1.5999999999999996 Lower Whisker, Upper Whisker : 7.1000000000000005 , 13.5 Out of 1599 rows in data, number of outliers are: 13
Boxplot of Signal_Strength Q1 is : 5.0 Q2 is : 6.0 Q3 is : 6.0 IQR is: 1.0 Lower Whisker, Upper Whisker : 3.5 , 7.5 Out of 1599 rows in data, number of outliers are: 28
Parameter 4 has the highest number of outliers which is 155.
X = mydata.drop("Signal_Strength", axis=1)
y = mydata['Signal_Strength']
# splitting to create test data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.30, random_state=seed)
print(X_train.shape)
print(X_test.shape)
(1119, 11) (480, 11)
# splitting to create training and validation data
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=.20, random_state=seed)
from sklearn.preprocessing import StandardScaler
# Scaling training data
X_Train_S = StandardScaler().fit_transform(X_train)
# Scaling testing data
X_Test_S = StandardScaler().fit_transform(X_test)
print(X_train.shape)
print(X_val.shape)
print(X_test.shape)
(895, 11) (224, 11) (480, 11)
print(y_train.shape)
print(y_val.shape)
print(y_test.shape)
(895,) (224,) (480,)
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Dense,BatchNormalization, Dropout, LeakyReLU
from tensorflow.keras import optimizers
NN_model_Regressor = Sequential()
# The Input Layer :
NN_model_Regressor.add(Dense(128, kernel_initializer='normal',input_dim = X_train.shape[1], activation='relu'))
# The Hidden Layers :
NN_model_Regressor.add(Dense(64, kernel_initializer='normal',activation='relu')) # sigmoid, tanh
NN_model_Regressor.add(Dense(32, kernel_initializer='normal'))
NN_model_Regressor.add(LeakyReLU(alpha=0.1))
NN_model_Regressor.add(Dense(16, kernel_initializer='normal'))
NN_model_Regressor.add(LeakyReLU(alpha=0.1))
# The Output Layer :
NN_model_Regressor.add(Dense(1, kernel_initializer='normal')) # except softmax
NN_model_Regressor.add(LeakyReLU(alpha=0.1))
# Compile the network :
NN_model_Regressor.compile(loss='mse', optimizer='adam',metrics=['accuracy'])
NN_model_Regressor.summary()
Model: "sequential_86" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_364 (Dense) (None, 128) 1536 _________________________________________________________________ dense_365 (Dense) (None, 64) 8256 _________________________________________________________________ dense_366 (Dense) (None, 32) 2080 _________________________________________________________________ leaky_re_lu_164 (LeakyReLU) (None, 32) 0 _________________________________________________________________ dense_367 (Dense) (None, 16) 528 _________________________________________________________________ leaky_re_lu_165 (LeakyReLU) (None, 16) 0 _________________________________________________________________ dense_368 (Dense) (None, 1) 17 _________________________________________________________________ leaky_re_lu_166 (LeakyReLU) (None, 1) 0 ================================================================= Total params: 12,417 Trainable params: 12,417 Non-trainable params: 0 _________________________________________________________________
EPOCH=500
Network_Regressor=NN_model_Regressor.fit(X_Train_S, y_train, validation_data=(X_Test_S,y_test), epochs=EPOCH, batch_size=200)
Epoch 1/500 5/5 [==============================] - 2s 120ms/step - loss: 32.4837 - accuracy: 0.0000e+00 - val_loss: 32.4086 - val_accuracy: 0.0000e+00 Epoch 2/500 5/5 [==============================] - 0s 22ms/step - loss: 32.3190 - accuracy: 0.0000e+00 - val_loss: 32.1814 - val_accuracy: 0.0000e+00 Epoch 3/500 5/5 [==============================] - 0s 18ms/step - loss: 32.0336 - accuracy: 0.0000e+00 - val_loss: 31.7573 - val_accuracy: 0.0000e+00 Epoch 4/500 5/5 [==============================] - 0s 15ms/step - loss: 31.4886 - accuracy: 0.0000e+00 - val_loss: 30.9303 - val_accuracy: 0.0000e+00 Epoch 5/500 5/5 [==============================] - 0s 16ms/step - loss: 30.4223 - accuracy: 0.0000e+00 - val_loss: 29.3253 - val_accuracy: 0.0000e+00 Epoch 6/500 5/5 [==============================] - 0s 16ms/step - loss: 28.4203 - accuracy: 0.0000e+00 - val_loss: 26.3561 - val_accuracy: 0.0000e+00 Epoch 7/500 5/5 [==============================] - 0s 17ms/step - loss: 24.7429 - accuracy: 0.0000e+00 - val_loss: 21.2913 - val_accuracy: 0.0000e+00 Epoch 8/500 5/5 [==============================] - 0s 16ms/step - loss: 18.8562 - accuracy: 0.0000e+00 - val_loss: 13.8197 - val_accuracy: 0.0000e+00 Epoch 9/500 5/5 [==============================] - 0s 17ms/step - loss: 10.8367 - accuracy: 0.0000e+00 - val_loss: 6.0118 - val_accuracy: 0.0000e+00 Epoch 10/500 5/5 [==============================] - 0s 15ms/step - loss: 4.6404 - accuracy: 0.0000e+00 - val_loss: 4.5022 - val_accuracy: 0.0000e+00 Epoch 11/500 5/5 [==============================] - 0s 16ms/step - loss: 4.8536 - accuracy: 0.0000e+00 - val_loss: 5.1978 - val_accuracy: 0.0000e+00 Epoch 12/500 5/5 [==============================] - 0s 18ms/step - loss: 3.9818 - accuracy: 0.0000e+00 - val_loss: 2.9646 - val_accuracy: 0.0000e+00 Epoch 13/500 5/5 [==============================] - 0s 20ms/step - loss: 2.5430 - accuracy: 0.0000e+00 - val_loss: 2.7755 - val_accuracy: 0.0000e+00 Epoch 14/500 5/5 [==============================] - 0s 20ms/step - loss: 2.5323 - accuracy: 0.0000e+00 - val_loss: 2.6219 - val_accuracy: 0.0000e+00 Epoch 15/500 5/5 [==============================] - 0s 20ms/step - loss: 2.2301 - accuracy: 0.0000e+00 - val_loss: 2.2198 - val_accuracy: 0.0000e+00 Epoch 16/500 5/5 [==============================] - 0s 20ms/step - loss: 1.9140 - accuracy: 0.0000e+00 - val_loss: 2.1551 - val_accuracy: 0.0000e+00 Epoch 17/500 5/5 [==============================] - 0s 29ms/step - loss: 1.8204 - accuracy: 0.0000e+00 - val_loss: 2.0674 - val_accuracy: 0.0000e+00 Epoch 18/500 5/5 [==============================] - 0s 25ms/step - loss: 1.7041 - accuracy: 0.0000e+00 - val_loss: 1.9101 - val_accuracy: 0.0000e+00 Epoch 19/500 5/5 [==============================] - 0s 28ms/step - loss: 1.6034 - accuracy: 0.0000e+00 - val_loss: 1.8404 - val_accuracy: 0.0000e+00 Epoch 20/500 5/5 [==============================] - 0s 27ms/step - loss: 1.5381 - accuracy: 0.0000e+00 - val_loss: 1.7752 - val_accuracy: 0.0000e+00 Epoch 21/500 5/5 [==============================] - 0s 23ms/step - loss: 1.4673 - accuracy: 0.0000e+00 - val_loss: 1.7258 - val_accuracy: 0.0000e+00 Epoch 22/500 5/5 [==============================] - 0s 24ms/step - loss: 1.4063 - accuracy: 0.0000e+00 - val_loss: 1.6842 - val_accuracy: 0.0000e+00 Epoch 23/500 5/5 [==============================] - 0s 27ms/step - loss: 1.3534 - accuracy: 0.0000e+00 - val_loss: 1.6244 - val_accuracy: 0.0000e+00 Epoch 24/500 5/5 [==============================] - 0s 27ms/step - loss: 1.3047 - accuracy: 0.0000e+00 - val_loss: 1.5756 - val_accuracy: 0.0000e+00 Epoch 25/500 5/5 [==============================] - 0s 28ms/step - loss: 1.2600 - accuracy: 0.0000e+00 - val_loss: 1.5389 - val_accuracy: 0.0000e+00 Epoch 26/500 5/5 [==============================] - 0s 29ms/step - loss: 1.2162 - accuracy: 0.0000e+00 - val_loss: 1.5025 - val_accuracy: 0.0000e+00 Epoch 27/500 5/5 [==============================] - 0s 16ms/step - loss: 1.1768 - accuracy: 0.0000e+00 - val_loss: 1.4598 - val_accuracy: 0.0000e+00 Epoch 28/500 5/5 [==============================] - 0s 15ms/step - loss: 1.1413 - accuracy: 0.0000e+00 - val_loss: 1.4157 - val_accuracy: 0.0000e+00 Epoch 29/500 5/5 [==============================] - 0s 15ms/step - loss: 1.1065 - accuracy: 0.0000e+00 - val_loss: 1.3823 - val_accuracy: 0.0000e+00 Epoch 30/500 5/5 [==============================] - 0s 16ms/step - loss: 1.0711 - accuracy: 0.0000e+00 - val_loss: 1.3540 - val_accuracy: 0.0000e+00 Epoch 31/500 5/5 [==============================] - 0s 17ms/step - loss: 1.0391 - accuracy: 0.0000e+00 - val_loss: 1.3283 - val_accuracy: 0.0000e+00 Epoch 32/500 5/5 [==============================] - 0s 16ms/step - loss: 1.0081 - accuracy: 0.0000e+00 - val_loss: 1.2983 - val_accuracy: 0.0000e+00 Epoch 33/500 5/5 [==============================] - 0s 39ms/step - loss: 0.9795 - accuracy: 0.0000e+00 - val_loss: 1.2638 - val_accuracy: 0.0000e+00 Epoch 34/500 5/5 [==============================] - 0s 28ms/step - loss: 0.9547 - accuracy: 0.0000e+00 - val_loss: 1.2347 - val_accuracy: 0.0000e+00 Epoch 35/500 5/5 [==============================] - 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0s 16ms/step - loss: 0.4909 - accuracy: 0.0000e+00 - val_loss: 0.6878 - val_accuracy: 0.0000e+00 Epoch 78/500 5/5 [==============================] - 0s 16ms/step - loss: 0.4912 - accuracy: 0.0000e+00 - val_loss: 0.6924 - val_accuracy: 0.0000e+00 Epoch 79/500 5/5 [==============================] - 0s 16ms/step - loss: 0.4840 - accuracy: 0.0000e+00 - val_loss: 0.6761 - val_accuracy: 0.0000e+00 Epoch 80/500 5/5 [==============================] - 0s 26ms/step - loss: 0.4797 - accuracy: 0.0000e+00 - val_loss: 0.6722 - val_accuracy: 0.0000e+00 Epoch 81/500 5/5 [==============================] - 0s 27ms/step - loss: 0.4766 - accuracy: 0.0000e+00 - val_loss: 0.6690 - val_accuracy: 0.0000e+00 Epoch 82/500 5/5 [==============================] - 0s 35ms/step - loss: 0.4731 - accuracy: 0.0000e+00 - val_loss: 0.6657 - val_accuracy: 0.0000e+00 Epoch 83/500 5/5 [==============================] - 0s 17ms/step - loss: 0.4693 - accuracy: 0.0000e+00 - val_loss: 0.6577 - val_accuracy: 0.0000e+00 Epoch 84/500 5/5 [==============================] - 0s 13ms/step - loss: 0.4692 - accuracy: 0.0000e+00 - val_loss: 0.6540 - val_accuracy: 0.0000e+00 Epoch 85/500 5/5 [==============================] - 0s 16ms/step - loss: 0.4675 - accuracy: 0.0000e+00 - val_loss: 0.6558 - val_accuracy: 0.0000e+00 Epoch 86/500 5/5 [==============================] - 0s 30ms/step - loss: 0.4631 - accuracy: 0.0000e+00 - val_loss: 0.6493 - val_accuracy: 0.0000e+00 Epoch 87/500 5/5 [==============================] - 0s 29ms/step - loss: 0.4600 - accuracy: 0.0000e+00 - val_loss: 0.6430 - val_accuracy: 0.0000e+00 Epoch 88/500 5/5 [==============================] - 0s 29ms/step - loss: 0.4580 - accuracy: 0.0000e+00 - val_loss: 0.6416 - val_accuracy: 0.0000e+00 Epoch 89/500 5/5 [==============================] - 0s 29ms/step - loss: 0.4568 - accuracy: 0.0000e+00 - val_loss: 0.6404 - val_accuracy: 0.0000e+00 Epoch 90/500 5/5 [==============================] - 0s 29ms/step - loss: 0.4532 - accuracy: 0.0000e+00 - val_loss: 0.6350 - val_accuracy: 0.0000e+00 Epoch 91/500 5/5 [==============================] - 0s 30ms/step - loss: 0.4514 - accuracy: 0.0000e+00 - val_loss: 0.6309 - val_accuracy: 0.0000e+00 Epoch 92/500 5/5 [==============================] - 0s 16ms/step - loss: 0.4503 - accuracy: 0.0000e+00 - val_loss: 0.6253 - val_accuracy: 0.0000e+00 Epoch 93/500 5/5 [==============================] - 0s 15ms/step - loss: 0.4460 - accuracy: 0.0000e+00 - val_loss: 0.6294 - val_accuracy: 0.0000e+00 Epoch 94/500 5/5 [==============================] - 0s 34ms/step - loss: 0.4480 - accuracy: 0.0000e+00 - val_loss: 0.6210 - val_accuracy: 0.0000e+00 Epoch 95/500 5/5 [==============================] - 0s 28ms/step - loss: 0.4453 - accuracy: 0.0000e+00 - val_loss: 0.6165 - val_accuracy: 0.0000e+00 Epoch 96/500 5/5 [==============================] - 0s 28ms/step - loss: 0.4420 - accuracy: 0.0000e+00 - val_loss: 0.6189 - val_accuracy: 0.0000e+00 Epoch 97/500 5/5 [==============================] - 0s 28ms/step - loss: 0.4457 - accuracy: 0.0000e+00 - val_loss: 0.6265 - val_accuracy: 0.0000e+00 Epoch 98/500 5/5 [==============================] - 0s 33ms/step - loss: 0.4410 - accuracy: 0.0000e+00 - val_loss: 0.6099 - val_accuracy: 0.0000e+00 Epoch 99/500 5/5 [==============================] - 0s 35ms/step - loss: 0.4381 - accuracy: 0.0000e+00 - val_loss: 0.6085 - val_accuracy: 0.0000e+00 Epoch 100/500 5/5 [==============================] - 0s 14ms/step - loss: 0.4370 - accuracy: 0.0000e+00 - val_loss: 0.6087 - val_accuracy: 0.0000e+00 Epoch 101/500 5/5 [==============================] - 0s 11ms/step - loss: 0.4339 - accuracy: 0.0000e+00 - val_loss: 0.6018 - val_accuracy: 0.0000e+00 Epoch 102/500 5/5 [==============================] - 0s 11ms/step - loss: 0.4339 - accuracy: 0.0000e+00 - val_loss: 0.5986 - val_accuracy: 0.0000e+00 Epoch 103/500 5/5 [==============================] - 0s 25ms/step - loss: 0.4312 - accuracy: 0.0000e+00 - val_loss: 0.5976 - val_accuracy: 0.0000e+00 Epoch 104/500 5/5 [==============================] - 0s 29ms/step - loss: 0.4291 - accuracy: 0.0000e+00 - val_loss: 0.6000 - val_accuracy: 0.0000e+00 Epoch 105/500 5/5 [==============================] - 0s 32ms/step - loss: 0.4282 - accuracy: 0.0000e+00 - val_loss: 0.5910 - val_accuracy: 0.0000e+00 Epoch 106/500 5/5 [==============================] - 0s 33ms/step - loss: 0.4279 - accuracy: 0.0000e+00 - val_loss: 0.5870 - val_accuracy: 0.0000e+00 Epoch 107/500 5/5 [==============================] - 0s 30ms/step - loss: 0.4260 - accuracy: 0.0000e+00 - val_loss: 0.5904 - val_accuracy: 0.0000e+00 Epoch 108/500 5/5 [==============================] - 0s 28ms/step - loss: 0.4269 - accuracy: 0.0000e+00 - val_loss: 0.5922 - val_accuracy: 0.0000e+00 Epoch 109/500 5/5 [==============================] - 0s 31ms/step - loss: 0.4243 - accuracy: 0.0000e+00 - val_loss: 0.5862 - val_accuracy: 0.0000e+00 Epoch 110/500 5/5 [==============================] - 0s 13ms/step - loss: 0.4224 - accuracy: 0.0000e+00 - val_loss: 0.5842 - val_accuracy: 0.0000e+00 Epoch 111/500 5/5 [==============================] - 0s 13ms/step - loss: 0.4219 - accuracy: 0.0000e+00 - val_loss: 0.5853 - val_accuracy: 0.0000e+00 Epoch 112/500 5/5 [==============================] - 0s 25ms/step - loss: 0.4216 - accuracy: 0.0000e+00 - val_loss: 0.5796 - val_accuracy: 0.0000e+00 Epoch 113/500 5/5 [==============================] - 0s 31ms/step - loss: 0.4197 - accuracy: 0.0000e+00 - val_loss: 0.5782 - val_accuracy: 0.0000e+00 Epoch 114/500 5/5 [==============================] - 0s 29ms/step - loss: 0.4179 - accuracy: 0.0000e+00 - val_loss: 0.5783 - val_accuracy: 0.0000e+00 Epoch 115/500 5/5 [==============================] - 0s 27ms/step - loss: 0.4165 - accuracy: 0.0000e+00 - val_loss: 0.5761 - val_accuracy: 0.0000e+00 Epoch 116/500 5/5 [==============================] - 0s 34ms/step - loss: 0.4161 - accuracy: 0.0000e+00 - val_loss: 0.5732 - val_accuracy: 0.0000e+00 Epoch 117/500 5/5 [==============================] - 0s 32ms/step - loss: 0.4156 - accuracy: 0.0000e+00 - val_loss: 0.5693 - val_accuracy: 0.0000e+00 Epoch 118/500 5/5 [==============================] - 0s 24ms/step - loss: 0.4167 - accuracy: 0.0000e+00 - val_loss: 0.5702 - val_accuracy: 0.0000e+00 Epoch 119/500 5/5 [==============================] - 0s 12ms/step - loss: 0.4138 - accuracy: 0.0000e+00 - val_loss: 0.5697 - val_accuracy: 0.0000e+00 Epoch 120/500 5/5 [==============================] - 0s 34ms/step - loss: 0.4125 - accuracy: 0.0000e+00 - val_loss: 0.5669 - val_accuracy: 0.0000e+00 Epoch 121/500 5/5 [==============================] - 0s 32ms/step - loss: 0.4125 - accuracy: 0.0000e+00 - val_loss: 0.5683 - val_accuracy: 0.0000e+00 Epoch 122/500 5/5 [==============================] - 0s 32ms/step - loss: 0.4117 - accuracy: 0.0000e+00 - val_loss: 0.5684 - val_accuracy: 0.0000e+00 Epoch 123/500 5/5 [==============================] - 0s 28ms/step - loss: 0.4123 - accuracy: 0.0000e+00 - val_loss: 0.5639 - val_accuracy: 0.0000e+00 Epoch 124/500 5/5 [==============================] - 0s 28ms/step - loss: 0.4100 - accuracy: 0.0000e+00 - val_loss: 0.5644 - val_accuracy: 0.0000e+00 Epoch 125/500 5/5 [==============================] - 0s 29ms/step - loss: 0.4094 - accuracy: 0.0000e+00 - val_loss: 0.5652 - val_accuracy: 0.0000e+00 Epoch 126/500 5/5 [==============================] - 0s 32ms/step - loss: 0.4098 - accuracy: 0.0000e+00 - val_loss: 0.5605 - val_accuracy: 0.0000e+00 Epoch 127/500 5/5 [==============================] - 0s 30ms/step - loss: 0.4091 - accuracy: 0.0000e+00 - val_loss: 0.5565 - val_accuracy: 0.0000e+00 Epoch 128/500 5/5 [==============================] - 0s 27ms/step - loss: 0.4062 - accuracy: 0.0000e+00 - val_loss: 0.5614 - val_accuracy: 0.0000e+00 Epoch 129/500 5/5 [==============================] - 0s 27ms/step - loss: 0.4074 - accuracy: 0.0000e+00 - val_loss: 0.5637 - val_accuracy: 0.0000e+00 Epoch 130/500 5/5 [==============================] - 0s 31ms/step - loss: 0.4064 - accuracy: 0.0000e+00 - val_loss: 0.5575 - val_accuracy: 0.0000e+00 Epoch 131/500 5/5 [==============================] - 0s 32ms/step - loss: 0.4071 - accuracy: 0.0000e+00 - val_loss: 0.5564 - val_accuracy: 0.0000e+00 Epoch 132/500 5/5 [==============================] - 0s 31ms/step - loss: 0.4057 - accuracy: 0.0000e+00 - val_loss: 0.5589 - val_accuracy: 0.0000e+00 Epoch 133/500 5/5 [==============================] - 0s 26ms/step - loss: 0.4058 - accuracy: 0.0000e+00 - val_loss: 0.5558 - val_accuracy: 0.0000e+00 Epoch 134/500 5/5 [==============================] - 0s 11ms/step - loss: 0.4032 - accuracy: 0.0000e+00 - val_loss: 0.5529 - val_accuracy: 0.0000e+00 Epoch 135/500 5/5 [==============================] - 0s 11ms/step - loss: 0.4032 - accuracy: 0.0000e+00 - val_loss: 0.5516 - val_accuracy: 0.0000e+00 Epoch 136/500 5/5 [==============================] - 0s 12ms/step - loss: 0.4033 - accuracy: 0.0000e+00 - val_loss: 0.5530 - val_accuracy: 0.0000e+00 Epoch 137/500 5/5 [==============================] - 0s 20ms/step - loss: 0.4021 - accuracy: 0.0000e+00 - val_loss: 0.5479 - val_accuracy: 0.0000e+00 Epoch 138/500 5/5 [==============================] - 0s 29ms/step - loss: 0.4019 - accuracy: 0.0000e+00 - val_loss: 0.5481 - val_accuracy: 0.0000e+00 Epoch 139/500 5/5 [==============================] - 0s 27ms/step - loss: 0.4041 - accuracy: 0.0000e+00 - val_loss: 0.5516 - val_accuracy: 0.0000e+00 Epoch 140/500 5/5 [==============================] - 0s 29ms/step - loss: 0.4016 - accuracy: 0.0000e+00 - val_loss: 0.5463 - val_accuracy: 0.0000e+00 Epoch 141/500 5/5 [==============================] - 0s 28ms/step - loss: 0.4025 - accuracy: 0.0000e+00 - val_loss: 0.5510 - val_accuracy: 0.0000e+00 Epoch 142/500 5/5 [==============================] - 0s 28ms/step - loss: 0.3997 - accuracy: 0.0000e+00 - val_loss: 0.5489 - val_accuracy: 0.0000e+00 Epoch 143/500 5/5 [==============================] - 0s 32ms/step - loss: 0.3992 - accuracy: 0.0000e+00 - val_loss: 0.5453 - val_accuracy: 0.0000e+00 Epoch 144/500 5/5 [==============================] - 0s 15ms/step - loss: 0.3980 - accuracy: 0.0000e+00 - val_loss: 0.5458 - val_accuracy: 0.0000e+00 Epoch 145/500 5/5 [==============================] - 0s 10ms/step - loss: 0.3990 - accuracy: 0.0000e+00 - val_loss: 0.5449 - val_accuracy: 0.0000e+00 Epoch 146/500 5/5 [==============================] - 0s 10ms/step - loss: 0.4018 - accuracy: 0.0000e+00 - val_loss: 0.5389 - val_accuracy: 0.0000e+00 Epoch 147/500 5/5 [==============================] - 0s 13ms/step - loss: 0.4003 - accuracy: 0.0000e+00 - val_loss: 0.5443 - val_accuracy: 0.0000e+00 Epoch 148/500 5/5 [==============================] - 0s 28ms/step - loss: 0.3988 - accuracy: 0.0000e+00 - val_loss: 0.5390 - val_accuracy: 0.0000e+00 Epoch 149/500 5/5 [==============================] - 0s 30ms/step - loss: 0.3969 - accuracy: 0.0000e+00 - val_loss: 0.5420 - val_accuracy: 0.0000e+00 Epoch 150/500 5/5 [==============================] - 0s 28ms/step - loss: 0.3952 - accuracy: 0.0000e+00 - val_loss: 0.5403 - val_accuracy: 0.0000e+00 Epoch 151/500 5/5 [==============================] - 0s 29ms/step - loss: 0.3966 - accuracy: 0.0000e+00 - val_loss: 0.5389 - val_accuracy: 0.0000e+00 Epoch 152/500 5/5 [==============================] - 0s 32ms/step - loss: 0.3951 - accuracy: 0.0000e+00 - val_loss: 0.5356 - val_accuracy: 0.0000e+00 Epoch 153/500 5/5 [==============================] - 0s 32ms/step - loss: 0.3948 - accuracy: 0.0000e+00 - val_loss: 0.5394 - val_accuracy: 0.0000e+00 Epoch 154/500 5/5 [==============================] - 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0s 29ms/step - loss: 0.3762 - accuracy: 0.0000e+00 - val_loss: 0.5145 - val_accuracy: 0.0000e+00 Epoch 218/500 5/5 [==============================] - 0s 29ms/step - loss: 0.3751 - accuracy: 0.0000e+00 - val_loss: 0.5155 - val_accuracy: 0.0000e+00 Epoch 219/500 5/5 [==============================] - 0s 29ms/step - loss: 0.3761 - accuracy: 0.0000e+00 - val_loss: 0.5133 - val_accuracy: 0.0000e+00 Epoch 220/500 5/5 [==============================] - 0s 27ms/step - loss: 0.3791 - accuracy: 0.0000e+00 - val_loss: 0.5126 - val_accuracy: 0.0000e+00 Epoch 221/500 5/5 [==============================] - 0s 11ms/step - loss: 0.3758 - accuracy: 0.0000e+00 - val_loss: 0.5123 - val_accuracy: 0.0000e+00 Epoch 222/500 5/5 [==============================] - 0s 11ms/step - loss: 0.3773 - accuracy: 0.0000e+00 - val_loss: 0.5138 - val_accuracy: 0.0000e+00 Epoch 223/500 5/5 [==============================] - 0s 11ms/step - loss: 0.3761 - accuracy: 0.0000e+00 - val_loss: 0.5123 - val_accuracy: 0.0000e+00 Epoch 224/500 5/5 [==============================] - 0s 12ms/step - loss: 0.3754 - accuracy: 0.0000e+00 - val_loss: 0.5133 - val_accuracy: 0.0000e+00 Epoch 225/500 5/5 [==============================] - 0s 11ms/step - loss: 0.3736 - accuracy: 0.0000e+00 - val_loss: 0.5121 - val_accuracy: 0.0000e+00 Epoch 226/500 5/5 [==============================] - 0s 10ms/step - loss: 0.3753 - accuracy: 0.0000e+00 - val_loss: 0.5171 - val_accuracy: 0.0000e+00 Epoch 227/500 5/5 [==============================] - 0s 11ms/step - loss: 0.3779 - accuracy: 0.0000e+00 - val_loss: 0.5155 - val_accuracy: 0.0000e+00 Epoch 228/500 5/5 [==============================] - 0s 10ms/step - loss: 0.3784 - accuracy: 0.0000e+00 - val_loss: 0.5109 - val_accuracy: 0.0000e+00 Epoch 229/500 5/5 [==============================] - 0s 10ms/step - loss: 0.3713 - accuracy: 0.0000e+00 - val_loss: 0.5239 - val_accuracy: 0.0000e+00 Epoch 230/500 5/5 [==============================] - 0s 12ms/step - loss: 0.3772 - accuracy: 0.0000e+00 - val_loss: 0.5099 - val_accuracy: 0.0000e+00 Epoch 231/500 5/5 [==============================] - 0s 10ms/step - loss: 0.3764 - accuracy: 0.0000e+00 - val_loss: 0.5106 - val_accuracy: 0.0000e+00 Epoch 232/500 5/5 [==============================] - 0s 10ms/step - loss: 0.3727 - accuracy: 0.0000e+00 - val_loss: 0.5179 - val_accuracy: 0.0000e+00 Epoch 233/500 5/5 [==============================] - 0s 10ms/step - loss: 0.3704 - accuracy: 0.0000e+00 - val_loss: 0.5090 - val_accuracy: 0.0000e+00 Epoch 234/500 5/5 [==============================] - 0s 11ms/step - loss: 0.3732 - accuracy: 0.0000e+00 - val_loss: 0.5094 - val_accuracy: 0.0000e+00 Epoch 235/500 5/5 [==============================] - 0s 27ms/step - loss: 0.3703 - accuracy: 0.0000e+00 - val_loss: 0.5104 - val_accuracy: 0.0000e+00 Epoch 236/500 5/5 [==============================] - 0s 32ms/step - loss: 0.3701 - accuracy: 0.0000e+00 - val_loss: 0.5107 - val_accuracy: 0.0000e+00 Epoch 237/500 5/5 [==============================] - 0s 14ms/step - loss: 0.3695 - accuracy: 0.0000e+00 - val_loss: 0.5082 - val_accuracy: 0.0000e+00 Epoch 238/500 5/5 [==============================] - 0s 11ms/step - loss: 0.3701 - accuracy: 0.0000e+00 - val_loss: 0.5101 - val_accuracy: 0.0000e+00 Epoch 239/500 5/5 [==============================] - 0s 11ms/step - loss: 0.3686 - accuracy: 0.0000e+00 - val_loss: 0.5068 - val_accuracy: 0.0000e+00 Epoch 240/500 5/5 [==============================] - 0s 11ms/step - loss: 0.3684 - accuracy: 0.0000e+00 - val_loss: 0.5122 - val_accuracy: 0.0000e+00 Epoch 241/500 5/5 [==============================] - 0s 11ms/step - loss: 0.3691 - accuracy: 0.0000e+00 - val_loss: 0.5073 - val_accuracy: 0.0000e+00 Epoch 242/500 5/5 [==============================] - 0s 10ms/step - loss: 0.3678 - accuracy: 0.0000e+00 - val_loss: 0.5041 - val_accuracy: 0.0000e+00 Epoch 243/500 5/5 [==============================] - 0s 11ms/step - loss: 0.3653 - accuracy: 0.0000e+00 - val_loss: 0.5054 - val_accuracy: 0.0000e+00 Epoch 244/500 5/5 [==============================] - 0s 25ms/step - loss: 0.3697 - accuracy: 0.0000e+00 - val_loss: 0.5043 - val_accuracy: 0.0000e+00 Epoch 245/500 5/5 [==============================] - 0s 26ms/step - loss: 0.3692 - accuracy: 0.0000e+00 - val_loss: 0.5077 - val_accuracy: 0.0000e+00 Epoch 246/500 5/5 [==============================] - 0s 28ms/step - loss: 0.3656 - accuracy: 0.0000e+00 - val_loss: 0.5058 - val_accuracy: 0.0000e+00 Epoch 247/500 5/5 [==============================] - 0s 29ms/step - loss: 0.3681 - accuracy: 0.0000e+00 - val_loss: 0.5086 - val_accuracy: 0.0000e+00 Epoch 248/500 5/5 [==============================] - 0s 29ms/step - loss: 0.3681 - accuracy: 0.0000e+00 - val_loss: 0.5028 - val_accuracy: 0.0000e+00 Epoch 249/500 5/5 [==============================] - 0s 26ms/step - loss: 0.3643 - accuracy: 0.0000e+00 - val_loss: 0.4997 - val_accuracy: 0.0000e+00 Epoch 250/500 5/5 [==============================] - 0s 14ms/step - loss: 0.3656 - accuracy: 0.0000e+00 - val_loss: 0.5069 - val_accuracy: 0.0000e+00 Epoch 251/500 5/5 [==============================] - 0s 15ms/step - loss: 0.3694 - accuracy: 0.0000e+00 - val_loss: 0.5063 - val_accuracy: 0.0000e+00 Epoch 252/500 5/5 [==============================] - 0s 28ms/step - loss: 0.3630 - accuracy: 0.0000e+00 - val_loss: 0.5034 - val_accuracy: 0.0000e+00 Epoch 253/500 5/5 [==============================] - 0s 28ms/step - loss: 0.3616 - accuracy: 0.0000e+00 - val_loss: 0.5050 - val_accuracy: 0.0000e+00 Epoch 254/500 5/5 [==============================] - 0s 27ms/step - loss: 0.3611 - accuracy: 0.0000e+00 - val_loss: 0.5024 - val_accuracy: 0.0000e+00 Epoch 255/500 5/5 [==============================] - 0s 27ms/step - loss: 0.3606 - accuracy: 0.0000e+00 - val_loss: 0.5023 - val_accuracy: 0.0000e+00 Epoch 256/500 5/5 [==============================] - 0s 24ms/step - loss: 0.3609 - accuracy: 0.0000e+00 - val_loss: 0.5009 - val_accuracy: 0.0000e+00 Epoch 257/500 5/5 [==============================] - 0s 25ms/step - loss: 0.3604 - accuracy: 0.0000e+00 - val_loss: 0.5004 - val_accuracy: 0.0000e+00 Epoch 258/500 5/5 [==============================] - 0s 26ms/step - loss: 0.3606 - accuracy: 0.0000e+00 - val_loss: 0.5035 - val_accuracy: 0.0000e+00 Epoch 259/500 5/5 [==============================] - 0s 12ms/step - loss: 0.3600 - accuracy: 0.0000e+00 - val_loss: 0.5035 - val_accuracy: 0.0000e+00 Epoch 260/500 5/5 [==============================] - 0s 10ms/step - loss: 0.3614 - accuracy: 0.0000e+00 - val_loss: 0.5026 - val_accuracy: 0.0000e+00 Epoch 261/500 5/5 [==============================] - 0s 12ms/step - loss: 0.3588 - accuracy: 0.0000e+00 - val_loss: 0.5022 - val_accuracy: 0.0000e+00 Epoch 262/500 5/5 [==============================] - 0s 15ms/step - loss: 0.3586 - accuracy: 0.0000e+00 - val_loss: 0.5007 - val_accuracy: 0.0000e+00 Epoch 263/500 5/5 [==============================] - 0s 16ms/step - loss: 0.3585 - accuracy: 0.0000e+00 - val_loss: 0.4964 - val_accuracy: 0.0000e+00 Epoch 264/500 5/5 [==============================] - 0s 16ms/step - loss: 0.3579 - accuracy: 0.0000e+00 - val_loss: 0.4962 - val_accuracy: 0.0000e+00 Epoch 265/500 5/5 [==============================] - 0s 18ms/step - loss: 0.3561 - accuracy: 0.0000e+00 - val_loss: 0.4997 - val_accuracy: 0.0000e+00 Epoch 266/500 5/5 [==============================] - 0s 17ms/step - loss: 0.3555 - accuracy: 0.0000e+00 - val_loss: 0.4977 - val_accuracy: 0.0000e+00 Epoch 267/500 5/5 [==============================] - 0s 19ms/step - loss: 0.3563 - accuracy: 0.0000e+00 - val_loss: 0.4976 - val_accuracy: 0.0000e+00 Epoch 268/500 5/5 [==============================] - 0s 16ms/step - loss: 0.3560 - accuracy: 0.0000e+00 - val_loss: 0.4978 - val_accuracy: 0.0000e+00 Epoch 269/500 5/5 [==============================] - 0s 15ms/step - loss: 0.3576 - accuracy: 0.0000e+00 - val_loss: 0.4971 - val_accuracy: 0.0000e+00 Epoch 270/500 5/5 [==============================] - 0s 27ms/step - loss: 0.3594 - accuracy: 0.0000e+00 - val_loss: 0.5009 - val_accuracy: 0.0000e+00 Epoch 271/500 5/5 [==============================] - 0s 31ms/step - loss: 0.3578 - accuracy: 0.0000e+00 - val_loss: 0.4962 - val_accuracy: 0.0000e+00 Epoch 272/500 5/5 [==============================] - 0s 12ms/step - loss: 0.3539 - accuracy: 0.0000e+00 - val_loss: 0.5025 - val_accuracy: 0.0000e+00 Epoch 273/500 5/5 [==============================] - 0s 12ms/step - loss: 0.3550 - accuracy: 0.0000e+00 - val_loss: 0.4945 - val_accuracy: 0.0000e+00 Epoch 274/500 5/5 [==============================] - 0s 12ms/step - loss: 0.3622 - accuracy: 0.0000e+00 - val_loss: 0.4947 - val_accuracy: 0.0000e+00 Epoch 275/500 5/5 [==============================] - 0s 13ms/step - loss: 0.3543 - accuracy: 0.0000e+00 - val_loss: 0.4997 - val_accuracy: 0.0000e+00 Epoch 276/500 5/5 [==============================] - 0s 14ms/step - loss: 0.3521 - accuracy: 0.0000e+00 - val_loss: 0.4907 - val_accuracy: 0.0000e+00 Epoch 277/500 5/5 [==============================] - 0s 16ms/step - loss: 0.3516 - accuracy: 0.0000e+00 - val_loss: 0.4964 - val_accuracy: 0.0000e+00 Epoch 278/500 5/5 [==============================] - 0s 18ms/step - loss: 0.3548 - accuracy: 0.0000e+00 - val_loss: 0.4905 - val_accuracy: 0.0000e+00 Epoch 279/500 5/5 [==============================] - 0s 20ms/step - loss: 0.3596 - accuracy: 0.0000e+00 - val_loss: 0.4912 - val_accuracy: 0.0000e+00 Epoch 280/500 5/5 [==============================] - 0s 20ms/step - loss: 0.3533 - accuracy: 0.0000e+00 - val_loss: 0.4999 - val_accuracy: 0.0000e+00 Epoch 281/500 5/5 [==============================] - 0s 21ms/step - loss: 0.3564 - accuracy: 0.0000e+00 - val_loss: 0.4878 - val_accuracy: 0.0000e+00 Epoch 282/500 5/5 [==============================] - 0s 21ms/step - loss: 0.3507 - accuracy: 0.0000e+00 - val_loss: 0.4990 - val_accuracy: 0.0000e+00 Epoch 283/500 5/5 [==============================] - 0s 22ms/step - loss: 0.3524 - accuracy: 0.0000e+00 - val_loss: 0.4911 - val_accuracy: 0.0000e+00 Epoch 284/500 5/5 [==============================] - 0s 26ms/step - loss: 0.3533 - accuracy: 0.0000e+00 - val_loss: 0.4975 - val_accuracy: 0.0000e+00 Epoch 285/500 5/5 [==============================] - 0s 15ms/step - loss: 0.3491 - accuracy: 0.0000e+00 - val_loss: 0.4939 - val_accuracy: 0.0000e+00 Epoch 286/500 5/5 [==============================] - 0s 18ms/step - loss: 0.3512 - accuracy: 0.0000e+00 - val_loss: 0.4911 - val_accuracy: 0.0000e+00 Epoch 287/500 5/5 [==============================] - 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0s 14ms/step - loss: 0.3530 - accuracy: 0.0000e+00 - val_loss: 0.4937 - val_accuracy: 0.0000e+00 Epoch 295/500 5/5 [==============================] - 0s 14ms/step - loss: 0.3460 - accuracy: 0.0000e+00 - val_loss: 0.4850 - val_accuracy: 0.0000e+00 Epoch 296/500 5/5 [==============================] - 0s 17ms/step - loss: 0.3496 - accuracy: 0.0000e+00 - val_loss: 0.4898 - val_accuracy: 0.0000e+00 Epoch 297/500 5/5 [==============================] - 0s 29ms/step - loss: 0.3436 - accuracy: 0.0000e+00 - val_loss: 0.4916 - val_accuracy: 0.0000e+00 Epoch 298/500 5/5 [==============================] - 0s 30ms/step - loss: 0.3427 - accuracy: 0.0000e+00 - val_loss: 0.4914 - val_accuracy: 0.0000e+00 Epoch 299/500 5/5 [==============================] - 0s 29ms/step - loss: 0.3434 - accuracy: 0.0000e+00 - val_loss: 0.4839 - val_accuracy: 0.0000e+00 Epoch 300/500 5/5 [==============================] - 0s 37ms/step - loss: 0.3426 - accuracy: 0.0000e+00 - val_loss: 0.4877 - val_accuracy: 0.0000e+00 Epoch 301/500 5/5 [==============================] - 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0s 11ms/step - loss: 0.3151 - accuracy: 0.0000e+00 - val_loss: 0.4893 - val_accuracy: 0.0000e+00 Epoch 372/500 5/5 [==============================] - 0s 11ms/step - loss: 0.3135 - accuracy: 0.0000e+00 - val_loss: 0.4837 - val_accuracy: 0.0000e+00 Epoch 373/500 5/5 [==============================] - 0s 13ms/step - loss: 0.3131 - accuracy: 0.0000e+00 - val_loss: 0.4870 - val_accuracy: 0.0000e+00 Epoch 374/500 5/5 [==============================] - 0s 15ms/step - loss: 0.3188 - accuracy: 0.0000e+00 - val_loss: 0.4815 - val_accuracy: 0.0000e+00 Epoch 375/500 5/5 [==============================] - 0s 30ms/step - loss: 0.3183 - accuracy: 0.0000e+00 - val_loss: 0.4936 - val_accuracy: 0.0000e+00 Epoch 376/500 5/5 [==============================] - 0s 30ms/step - loss: 0.3126 - accuracy: 0.0000e+00 - val_loss: 0.4834 - val_accuracy: 0.0000e+00 Epoch 377/500 5/5 [==============================] - 0s 31ms/step - loss: 0.3094 - accuracy: 0.0000e+00 - val_loss: 0.4805 - val_accuracy: 0.0000e+00 Epoch 378/500 5/5 [==============================] - 0s 13ms/step - loss: 0.3091 - accuracy: 0.0000e+00 - val_loss: 0.4856 - val_accuracy: 0.0000e+00 Epoch 379/500 5/5 [==============================] - 0s 11ms/step - loss: 0.3077 - accuracy: 0.0000e+00 - val_loss: 0.4839 - val_accuracy: 0.0000e+00 Epoch 380/500 5/5 [==============================] - 0s 12ms/step - loss: 0.3098 - accuracy: 0.0000e+00 - val_loss: 0.4858 - val_accuracy: 0.0000e+00 Epoch 381/500 5/5 [==============================] - 0s 31ms/step - loss: 0.3088 - accuracy: 0.0000e+00 - val_loss: 0.4852 - val_accuracy: 0.0000e+00 Epoch 382/500 5/5 [==============================] - 0s 20ms/step - loss: 0.3059 - accuracy: 0.0000e+00 - val_loss: 0.4802 - val_accuracy: 0.0000e+00 Epoch 383/500 5/5 [==============================] - 0s 12ms/step - loss: 0.3074 - accuracy: 0.0000e+00 - val_loss: 0.4818 - val_accuracy: 0.0000e+00 Epoch 384/500 5/5 [==============================] - 0s 12ms/step - loss: 0.3080 - accuracy: 0.0000e+00 - val_loss: 0.4869 - val_accuracy: 0.0000e+00 Epoch 385/500 5/5 [==============================] - 0s 15ms/step - loss: 0.3129 - accuracy: 0.0000e+00 - val_loss: 0.4865 - val_accuracy: 0.0000e+00 Epoch 386/500 5/5 [==============================] - 0s 17ms/step - loss: 0.3118 - accuracy: 0.0000e+00 - val_loss: 0.4832 - val_accuracy: 0.0000e+00 Epoch 387/500 5/5 [==============================] - 0s 18ms/step - loss: 0.3108 - accuracy: 0.0000e+00 - val_loss: 0.4846 - val_accuracy: 0.0000e+00 Epoch 388/500 5/5 [==============================] - 0s 23ms/step - loss: 0.3142 - accuracy: 0.0000e+00 - val_loss: 0.4970 - val_accuracy: 0.0000e+00 Epoch 389/500 5/5 [==============================] - 0s 22ms/step - loss: 0.3114 - accuracy: 0.0000e+00 - val_loss: 0.4811 - val_accuracy: 0.0000e+00 Epoch 390/500 5/5 [==============================] - 0s 21ms/step - loss: 0.3051 - accuracy: 0.0000e+00 - val_loss: 0.4837 - val_accuracy: 0.0000e+00 Epoch 391/500 5/5 [==============================] - 0s 22ms/step - loss: 0.3065 - accuracy: 0.0000e+00 - val_loss: 0.4871 - val_accuracy: 0.0000e+00 Epoch 392/500 5/5 [==============================] - 0s 22ms/step - loss: 0.3057 - accuracy: 0.0000e+00 - val_loss: 0.4822 - val_accuracy: 0.0000e+00 Epoch 393/500 5/5 [==============================] - 0s 23ms/step - loss: 0.3041 - accuracy: 0.0000e+00 - val_loss: 0.4845 - val_accuracy: 0.0000e+00 Epoch 394/500 5/5 [==============================] - 0s 22ms/step - loss: 0.3072 - accuracy: 0.0000e+00 - val_loss: 0.4837 - val_accuracy: 0.0000e+00 Epoch 395/500 5/5 [==============================] - 0s 21ms/step - loss: 0.3115 - accuracy: 0.0000e+00 - val_loss: 0.4882 - val_accuracy: 0.0000e+00 Epoch 396/500 5/5 [==============================] - 0s 27ms/step - loss: 0.3047 - accuracy: 0.0000e+00 - val_loss: 0.4859 - val_accuracy: 0.0000e+00 Epoch 397/500 5/5 [==============================] - 0s 28ms/step - loss: 0.3053 - accuracy: 0.0000e+00 - val_loss: 0.4855 - val_accuracy: 0.0000e+00 Epoch 398/500 5/5 [==============================] - 0s 26ms/step - loss: 0.3091 - accuracy: 0.0000e+00 - val_loss: 0.4802 - val_accuracy: 0.0000e+00 Epoch 399/500 5/5 [==============================] - 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0s 27ms/step - loss: 0.2966 - accuracy: 0.0000e+00 - val_loss: 0.4837 - val_accuracy: 0.0000e+00 Epoch 414/500 5/5 [==============================] - 0s 27ms/step - loss: 0.2959 - accuracy: 0.0000e+00 - val_loss: 0.4896 - val_accuracy: 0.0000e+00 Epoch 415/500 5/5 [==============================] - 0s 25ms/step - loss: 0.3033 - accuracy: 0.0000e+00 - val_loss: 0.4919 - val_accuracy: 0.0000e+00 Epoch 416/500 5/5 [==============================] - 0s 27ms/step - loss: 0.3025 - accuracy: 0.0000e+00 - val_loss: 0.4947 - val_accuracy: 0.0000e+00 Epoch 417/500 5/5 [==============================] - 0s 28ms/step - loss: 0.2989 - accuracy: 0.0000e+00 - val_loss: 0.4864 - val_accuracy: 0.0000e+00 Epoch 418/500 5/5 [==============================] - 0s 27ms/step - loss: 0.2949 - accuracy: 0.0000e+00 - val_loss: 0.4879 - val_accuracy: 0.0000e+00 Epoch 419/500 5/5 [==============================] - 0s 11ms/step - loss: 0.2941 - accuracy: 0.0000e+00 - val_loss: 0.4937 - val_accuracy: 0.0000e+00 Epoch 420/500 5/5 [==============================] - 0s 11ms/step - loss: 0.2947 - accuracy: 0.0000e+00 - val_loss: 0.4936 - val_accuracy: 0.0000e+00 Epoch 421/500 5/5 [==============================] - 0s 11ms/step - loss: 0.2965 - accuracy: 0.0000e+00 - val_loss: 0.4923 - val_accuracy: 0.0000e+00 Epoch 422/500 5/5 [==============================] - 0s 11ms/step - loss: 0.3002 - accuracy: 0.0000e+00 - val_loss: 0.4999 - val_accuracy: 0.0000e+00 Epoch 423/500 5/5 [==============================] - 0s 11ms/step - loss: 0.2934 - accuracy: 0.0000e+00 - val_loss: 0.4899 - val_accuracy: 0.0000e+00 Epoch 424/500 5/5 [==============================] - 0s 16ms/step - loss: 0.2930 - accuracy: 0.0000e+00 - val_loss: 0.4913 - val_accuracy: 0.0000e+00 Epoch 425/500 5/5 [==============================] - 0s 29ms/step - loss: 0.2910 - accuracy: 0.0000e+00 - val_loss: 0.4927 - val_accuracy: 0.0000e+00 Epoch 426/500 5/5 [==============================] - 0s 27ms/step - loss: 0.2945 - accuracy: 0.0000e+00 - val_loss: 0.4928 - val_accuracy: 0.0000e+00 Epoch 427/500 5/5 [==============================] - 0s 28ms/step - loss: 0.3016 - accuracy: 0.0000e+00 - val_loss: 0.4940 - val_accuracy: 0.0000e+00 Epoch 428/500 5/5 [==============================] - 0s 27ms/step - loss: 0.2929 - accuracy: 0.0000e+00 - val_loss: 0.4955 - val_accuracy: 0.0000e+00 Epoch 429/500 5/5 [==============================] - 0s 29ms/step - loss: 0.2928 - accuracy: 0.0000e+00 - val_loss: 0.5054 - val_accuracy: 0.0000e+00 Epoch 430/500 5/5 [==============================] - 0s 29ms/step - loss: 0.2970 - accuracy: 0.0000e+00 - val_loss: 0.4946 - val_accuracy: 0.0000e+00 Epoch 431/500 5/5 [==============================] - 0s 19ms/step - loss: 0.2898 - accuracy: 0.0000e+00 - val_loss: 0.4973 - val_accuracy: 0.0000e+00 Epoch 432/500 5/5 [==============================] - 0s 27ms/step - loss: 0.2982 - accuracy: 0.0000e+00 - val_loss: 0.5003 - val_accuracy: 0.0000e+00 Epoch 433/500 5/5 [==============================] - 0s 31ms/step - loss: 0.3126 - accuracy: 0.0000e+00 - val_loss: 0.5165 - val_accuracy: 0.0000e+00 Epoch 434/500 5/5 [==============================] - 0s 27ms/step - loss: 0.3152 - accuracy: 0.0000e+00 - val_loss: 0.4980 - val_accuracy: 0.0000e+00 Epoch 435/500 5/5 [==============================] - 0s 26ms/step - loss: 0.3021 - accuracy: 0.0000e+00 - val_loss: 0.5228 - val_accuracy: 0.0000e+00 Epoch 436/500 5/5 [==============================] - 0s 27ms/step - loss: 0.3015 - accuracy: 0.0000e+00 - val_loss: 0.4923 - val_accuracy: 0.0000e+00 Epoch 437/500 5/5 [==============================] - 0s 26ms/step - loss: 0.2973 - accuracy: 0.0000e+00 - val_loss: 0.5035 - val_accuracy: 0.0000e+00 Epoch 438/500 5/5 [==============================] - 0s 27ms/step - loss: 0.2900 - accuracy: 0.0000e+00 - val_loss: 0.4941 - val_accuracy: 0.0000e+00 Epoch 439/500 5/5 [==============================] - 0s 15ms/step - loss: 0.2902 - accuracy: 0.0000e+00 - val_loss: 0.5000 - val_accuracy: 0.0000e+00 Epoch 440/500 5/5 [==============================] - 0s 16ms/step - loss: 0.2929 - accuracy: 0.0000e+00 - val_loss: 0.4958 - val_accuracy: 0.0000e+00 Epoch 441/500 5/5 [==============================] - 0s 28ms/step - loss: 0.2931 - accuracy: 0.0000e+00 - val_loss: 0.4902 - val_accuracy: 0.0000e+00 Epoch 442/500 5/5 [==============================] - 0s 30ms/step - loss: 0.2886 - accuracy: 0.0000e+00 - val_loss: 0.4949 - val_accuracy: 0.0000e+00 Epoch 443/500 5/5 [==============================] - 0s 34ms/step - loss: 0.2863 - accuracy: 0.0000e+00 - val_loss: 0.4934 - val_accuracy: 0.0000e+00 Epoch 444/500 5/5 [==============================] - 0s 27ms/step - loss: 0.2887 - accuracy: 0.0000e+00 - val_loss: 0.5004 - val_accuracy: 0.0000e+00 Epoch 445/500 5/5 [==============================] - 0s 27ms/step - loss: 0.2874 - accuracy: 0.0000e+00 - val_loss: 0.4896 - val_accuracy: 0.0000e+00 Epoch 446/500 5/5 [==============================] - 0s 28ms/step - loss: 0.2895 - accuracy: 0.0000e+00 - val_loss: 0.4959 - val_accuracy: 0.0000e+00 Epoch 447/500 5/5 [==============================] - 0s 25ms/step - loss: 0.2847 - accuracy: 0.0000e+00 - val_loss: 0.5031 - val_accuracy: 0.0000e+00 Epoch 448/500 5/5 [==============================] - 0s 11ms/step - loss: 0.2896 - accuracy: 0.0000e+00 - val_loss: 0.4994 - val_accuracy: 0.0000e+00 Epoch 449/500 5/5 [==============================] - 0s 19ms/step - loss: 0.2868 - accuracy: 0.0000e+00 - val_loss: 0.4950 - val_accuracy: 0.0000e+00 Epoch 450/500 5/5 [==============================] - 0s 28ms/step - loss: 0.2895 - accuracy: 0.0000e+00 - val_loss: 0.5008 - val_accuracy: 0.0000e+00 Epoch 451/500 5/5 [==============================] - 0s 33ms/step - loss: 0.2863 - accuracy: 0.0000e+00 - val_loss: 0.4924 - val_accuracy: 0.0000e+00 Epoch 452/500 5/5 [==============================] - 0s 29ms/step - loss: 0.2851 - accuracy: 0.0000e+00 - val_loss: 0.5112 - val_accuracy: 0.0000e+00 Epoch 453/500 5/5 [==============================] - 0s 23ms/step - loss: 0.2848 - accuracy: 0.0000e+00 - val_loss: 0.4964 - val_accuracy: 0.0000e+00 Epoch 454/500 5/5 [==============================] - 0s 12ms/step - loss: 0.2940 - accuracy: 0.0000e+00 - val_loss: 0.5114 - val_accuracy: 0.0000e+00 Epoch 455/500 5/5 [==============================] - 0s 13ms/step - loss: 0.2888 - accuracy: 0.0000e+00 - val_loss: 0.5003 - val_accuracy: 0.0000e+00 Epoch 456/500 5/5 [==============================] - 0s 13ms/step - loss: 0.2857 - accuracy: 0.0000e+00 - val_loss: 0.5069 - val_accuracy: 0.0000e+00 Epoch 457/500 5/5 [==============================] - 0s 15ms/step - loss: 0.2859 - accuracy: 0.0000e+00 - val_loss: 0.4980 - val_accuracy: 0.0000e+00 Epoch 458/500 5/5 [==============================] - 0s 15ms/step - loss: 0.2834 - accuracy: 0.0000e+00 - val_loss: 0.5034 - val_accuracy: 0.0000e+00 Epoch 459/500 5/5 [==============================] - 0s 14ms/step - loss: 0.2848 - accuracy: 0.0000e+00 - val_loss: 0.4985 - val_accuracy: 0.0000e+00 Epoch 460/500 5/5 [==============================] - 0s 16ms/step - loss: 0.2824 - accuracy: 0.0000e+00 - val_loss: 0.4960 - val_accuracy: 0.0000e+00 Epoch 461/500 5/5 [==============================] - 0s 15ms/step - loss: 0.2817 - accuracy: 0.0000e+00 - val_loss: 0.4998 - val_accuracy: 0.0000e+00 Epoch 462/500 5/5 [==============================] - 0s 16ms/step - loss: 0.2832 - accuracy: 0.0000e+00 - val_loss: 0.4989 - val_accuracy: 0.0000e+00 Epoch 463/500 5/5 [==============================] - 0s 21ms/step - loss: 0.2815 - accuracy: 0.0000e+00 - val_loss: 0.4995 - val_accuracy: 0.0000e+00 Epoch 464/500 5/5 [==============================] - 0s 30ms/step - loss: 0.2851 - accuracy: 0.0000e+00 - val_loss: 0.5092 - val_accuracy: 0.0000e+00 Epoch 465/500 5/5 [==============================] - 0s 29ms/step - loss: 0.2858 - accuracy: 0.0000e+00 - val_loss: 0.5026 - val_accuracy: 0.0000e+00 Epoch 466/500 5/5 [==============================] - 0s 26ms/step - loss: 0.2864 - accuracy: 0.0000e+00 - val_loss: 0.5146 - val_accuracy: 0.0000e+00 Epoch 467/500 5/5 [==============================] - 0s 26ms/step - loss: 0.2872 - accuracy: 0.0000e+00 - val_loss: 0.4964 - val_accuracy: 0.0000e+00 Epoch 468/500 5/5 [==============================] - 0s 28ms/step - loss: 0.2833 - accuracy: 0.0000e+00 - val_loss: 0.5101 - val_accuracy: 0.0000e+00 Epoch 469/500 5/5 [==============================] - 0s 16ms/step - loss: 0.2805 - accuracy: 0.0000e+00 - val_loss: 0.4959 - val_accuracy: 0.0000e+00 Epoch 470/500 5/5 [==============================] - 0s 11ms/step - loss: 0.2838 - accuracy: 0.0000e+00 - val_loss: 0.5073 - val_accuracy: 0.0000e+00 Epoch 471/500 5/5 [==============================] - 0s 14ms/step - loss: 0.2807 - accuracy: 0.0000e+00 - val_loss: 0.5005 - val_accuracy: 0.0000e+00 Epoch 472/500 5/5 [==============================] - 0s 32ms/step - loss: 0.2869 - accuracy: 0.0000e+00 - val_loss: 0.5083 - val_accuracy: 0.0000e+00 Epoch 473/500 5/5 [==============================] - 0s 35ms/step - loss: 0.2835 - accuracy: 0.0000e+00 - val_loss: 0.5015 - val_accuracy: 0.0000e+00 Epoch 474/500 5/5 [==============================] - 0s 30ms/step - loss: 0.2768 - accuracy: 0.0000e+00 - val_loss: 0.5079 - val_accuracy: 0.0000e+00 Epoch 475/500 5/5 [==============================] - 0s 26ms/step - loss: 0.2809 - accuracy: 0.0000e+00 - val_loss: 0.5008 - val_accuracy: 0.0000e+00 Epoch 476/500 5/5 [==============================] - 0s 31ms/step - loss: 0.2819 - accuracy: 0.0000e+00 - val_loss: 0.5015 - val_accuracy: 0.0000e+00 Epoch 477/500 5/5 [==============================] - 0s 29ms/step - loss: 0.2858 - accuracy: 0.0000e+00 - val_loss: 0.5046 - val_accuracy: 0.0000e+00 Epoch 478/500 5/5 [==============================] - 0s 15ms/step - loss: 0.2765 - accuracy: 0.0000e+00 - val_loss: 0.5050 - val_accuracy: 0.0000e+00 Epoch 479/500 5/5 [==============================] - 0s 13ms/step - loss: 0.2778 - accuracy: 0.0000e+00 - val_loss: 0.5083 - val_accuracy: 0.0000e+00 Epoch 480/500 5/5 [==============================] - 0s 14ms/step - loss: 0.2762 - accuracy: 0.0000e+00 - val_loss: 0.5005 - val_accuracy: 0.0000e+00 Epoch 481/500 5/5 [==============================] - 0s 11ms/step - loss: 0.2750 - accuracy: 0.0000e+00 - val_loss: 0.5049 - val_accuracy: 0.0000e+00 Epoch 482/500 5/5 [==============================] - 0s 11ms/step - loss: 0.2775 - accuracy: 0.0000e+00 - val_loss: 0.5060 - val_accuracy: 0.0000e+00 Epoch 483/500 5/5 [==============================] - 0s 12ms/step - loss: 0.2775 - accuracy: 0.0000e+00 - val_loss: 0.4984 - val_accuracy: 0.0000e+00 Epoch 484/500 5/5 [==============================] - 0s 12ms/step - loss: 0.2753 - accuracy: 0.0000e+00 - val_loss: 0.5091 - val_accuracy: 0.0000e+00 Epoch 485/500 5/5 [==============================] - 0s 14ms/step - loss: 0.2837 - accuracy: 0.0000e+00 - val_loss: 0.5088 - val_accuracy: 0.0000e+00 Epoch 486/500 5/5 [==============================] - 0s 13ms/step - loss: 0.2786 - accuracy: 0.0000e+00 - val_loss: 0.5020 - val_accuracy: 0.0000e+00 Epoch 487/500 5/5 [==============================] - 0s 13ms/step - loss: 0.2804 - accuracy: 0.0000e+00 - val_loss: 0.5278 - val_accuracy: 0.0000e+00 Epoch 488/500 5/5 [==============================] - 0s 13ms/step - loss: 0.2843 - accuracy: 0.0000e+00 - val_loss: 0.5141 - val_accuracy: 0.0000e+00 Epoch 489/500 5/5 [==============================] - 0s 11ms/step - loss: 0.2851 - accuracy: 0.0000e+00 - val_loss: 0.5204 - val_accuracy: 0.0000e+00 Epoch 490/500 5/5 [==============================] - 0s 12ms/step - loss: 0.2792 - accuracy: 0.0000e+00 - val_loss: 0.5126 - val_accuracy: 0.0000e+00 Epoch 491/500 5/5 [==============================] - 0s 12ms/step - loss: 0.2793 - accuracy: 0.0000e+00 - val_loss: 0.5149 - val_accuracy: 0.0000e+00 Epoch 492/500 5/5 [==============================] - 0s 12ms/step - loss: 0.2776 - accuracy: 0.0000e+00 - val_loss: 0.5051 - val_accuracy: 0.0000e+00 Epoch 493/500 5/5 [==============================] - 0s 12ms/step - loss: 0.2755 - accuracy: 0.0000e+00 - val_loss: 0.5083 - val_accuracy: 0.0000e+00 Epoch 494/500 5/5 [==============================] - 0s 11ms/step - loss: 0.2758 - accuracy: 0.0000e+00 - val_loss: 0.5115 - val_accuracy: 0.0000e+00 Epoch 495/500 5/5 [==============================] - 0s 13ms/step - loss: 0.2758 - accuracy: 0.0000e+00 - val_loss: 0.5115 - val_accuracy: 0.0000e+00 Epoch 496/500 5/5 [==============================] - 0s 28ms/step - loss: 0.2744 - accuracy: 0.0000e+00 - val_loss: 0.5061 - val_accuracy: 0.0000e+00 Epoch 497/500 5/5 [==============================] - 0s 28ms/step - loss: 0.2807 - accuracy: 0.0000e+00 - val_loss: 0.5228 - val_accuracy: 0.0000e+00 Epoch 498/500 5/5 [==============================] - 0s 27ms/step - loss: 0.2771 - accuracy: 0.0000e+00 - val_loss: 0.5122 - val_accuracy: 0.0000e+00 Epoch 499/500 5/5 [==============================] - 0s 17ms/step - loss: 0.2757 - accuracy: 0.0000e+00 - val_loss: 0.5394 - val_accuracy: 0.0000e+00 Epoch 500/500 5/5 [==============================] - 0s 32ms/step - loss: 0.2842 - accuracy: 0.0000e+00 - val_loss: 0.5024 - val_accuracy: 0.0000e+00
loss_train = Network_Regressor.history['loss']
loss_val = Network_Regressor.history['val_loss']
epochs = range(1,EPOCH+1)
plt.plot(epochs, loss_train, 'g', label='Training loss')
plt.plot(epochs, loss_val, 'b', label='validation loss')
plt.title('Training and Validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()
from tensorflow.keras.models import model_from_json
# Pickle model to JSON
Regressor_model_json = NN_model_Regressor.to_json()
with open("Regressor_model.json", "w") as json_file:
json_file.write(Regressor_model_json)
# Pickle weights to HDF5
NN_model_Regressor.save_weights("Regressor_model.h5")
print("Saved model to disk")
# load json and create model
json_file = open('Regressor_model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("Regressor_model.h5")
print("Loaded model from disk")
# Evaluate
loaded_model.compile(loss='mse', optimizer='adam', metrics=['accuracy'])
score = loaded_model.evaluate(X_Test_S,y_test, verbose=1)
print("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100))
Saved model to disk Loaded model from disk 15/15 [==============================] - 1s 2ms/step - loss: 0.5024 - accuracy: 0.0000e+00 accuracy: 0.00%
# Seaborn is based on matplotlib, which aids in drawing attractive and informative statistical graphics.
import seaborn as sns
import tensorflow
print(tensorflow.__version__)
2.7.0
# suppress display of warnings
warnings.filterwarnings('ignore')
# display all dataframe columns
pd.options.display.max_columns = None
# to set the limit to 3 decimals
pd.options.display.float_format = '{:.7f}'.format
# display all dataframe rows
pd.options.display.max_rows = None
import h5py
# Open the file as readonly
h5f = h5py.File('Part - 4 - Autonomous_Vehicles_SVHN_single_grey1.h5', 'r')
h5f.keys()
<KeysViewHDF5 ['X_test', 'X_train', 'X_val', 'y_test', 'y_train', 'y_val']>
# Load the training, test and validation set
X_train = h5f['X_train'][:]
y_train = h5f['y_train'][:]
X_test = h5f['X_test'][:]
y_test = h5f['y_test'][:]
#Let us see the contents of features and labels of one example from the images
X_train[:1]
array([[[ 33.0704, 30.2601, 26.852 , ..., 71.4471, 58.2204,
42.9939],
[ 25.2283, 25.5533, 29.9765, ..., 113.0209, 103.3639,
84.2949],
[ 26.2775, 22.6137, 40.4763, ..., 113.3028, 121.775 ,
115.4228],
...,
[ 28.5502, 36.212 , 45.0801, ..., 24.1359, 25.0927,
26.0603],
[ 38.4352, 26.4733, 23.2717, ..., 28.1094, 29.4683,
30.0661],
[ 50.2984, 26.0773, 24.0389, ..., 49.6682, 50.853 ,
53.0377]]], dtype=float32)
y_train[:1]
array([2], dtype=uint8)
X_test[:1]
array([[[ 40.558 , 46.7917, 48.9764, ..., 112.1153, 112.9904,
112.1646],
[ 39.4379, 44.2911, 47.1768, ..., 111.0122, 110.9475,
109.9368],
[ 38.4488, 43.6394, 48.7098, ..., 109.8921, 109.9414,
109.1048],
...,
[ 34.9869, 35.4707, 39.6676, ..., 109.211 , 109.9074,
112.7346],
[ 35.6602, 35.5462, 40.3193, ..., 110.9998, 112.049 ,
114.3431],
[ 36.1871, 35.4214, 40.6998, ..., 110.0169, 111.2017,
114.1906]]], dtype=float32)
y_test[:1]
array([1], dtype=uint8)
# visualizing the first 10 images in the dataset and their labels
%matplotlib inline
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 1))
for i in range(10):
plt.subplot(1, 10, i+1)
plt.imshow(X_train[i], cmap="gray")
plt.axis('off')
plt.show()
print('label for each of the above image: %s' % (y_train[0:10]))
label for each of the above image: [2 6 7 4 4 0 3 0 7 3]
X_train.shape
(42000, 32, 32)
y_train.shape
(42000,)
Need to reshape the X_train and X_test so that the same can be fed for model building. We need to feed a 2D tensor into the model and currently we have a 3D tensor.
X_train = X_train.reshape(X_train.shape[0], 1024, 1)
X_test = X_test.reshape(X_test.shape[0], 1024, 1)
# # normalize inputs from 0-255 to 0-1
X_train = X_train / 255.0
X_test = X_test / 255.0
print('Resized Training set', X_train.shape, y_train.shape)
print('Resized Test set', X_test.shape, y_test.shape)
Resized Training set (42000, 1024, 1) (42000,) Resized Test set (18000, 1024, 1) (18000,)
Encoding the target variables. We need to one hot encode the labels for the model to understand the labels better. We will be using categorical cross entropy as our loss function and for this purpose we need our labels to be in one hot encoded format.
from tensorflow.keras.utils import to_categorical
# one hot encode outputs
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
# no.of classes
num_classes = y_test.shape[1]
print("The number of classes in this dataset are:",num_classes)
The number of classes in this dataset are: 10
# define model
from tensorflow.keras import optimizers
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
def nn_model():
# create model
model = Sequential()
model.add(Flatten())
model.add(Dense(256, activation='relu')) ###Multiple Dense units with Relu activation
model.add(Dense(64, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
return model
# build the model
model = nn_model()
# Compile model
sgd = optimizers.Adam(lr=1e-3)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) ### Loss function = Categorical cross entropy
# Fit the model
training_history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=200, batch_size=300, verbose=2)
Epoch 1/200 140/140 - 2s - loss: 2.3023 - accuracy: 0.1104 - val_loss: 2.2763 - val_accuracy: 0.1602 Epoch 2/200 140/140 - 1s - loss: 2.1142 - accuracy: 0.2288 - val_loss: 1.9043 - val_accuracy: 0.3217 Epoch 3/200 140/140 - 1s - loss: 1.7991 - accuracy: 0.3743 - val_loss: 1.6100 - val_accuracy: 0.4541 Epoch 4/200 140/140 - 1s - loss: 1.5231 - accuracy: 0.4901 - val_loss: 1.4489 - val_accuracy: 0.5077 Epoch 5/200 140/140 - 1s - loss: 1.3782 - accuracy: 0.5453 - val_loss: 1.3027 - val_accuracy: 0.5718 Epoch 6/200 140/140 - 1s - loss: 1.3049 - accuracy: 0.5741 - val_loss: 1.2819 - val_accuracy: 0.5788 Epoch 7/200 140/140 - 1s - loss: 1.2354 - accuracy: 0.6001 - val_loss: 1.2186 - val_accuracy: 0.6030 Epoch 8/200 140/140 - 1s - loss: 1.2025 - accuracy: 0.6098 - val_loss: 1.1725 - val_accuracy: 0.6238 Epoch 9/200 140/140 - 1s - loss: 1.1371 - accuracy: 0.6385 - val_loss: 1.1106 - val_accuracy: 0.6502 Epoch 10/200 140/140 - 1s - loss: 1.0831 - accuracy: 0.6580 - val_loss: 1.0579 - val_accuracy: 0.6671 Epoch 11/200 140/140 - 1s - loss: 1.0274 - accuracy: 0.6819 - val_loss: 1.0129 - val_accuracy: 0.6901 Epoch 12/200 140/140 - 1s - loss: 0.9906 - accuracy: 0.6955 - val_loss: 1.0500 - val_accuracy: 0.6724 Epoch 13/200 140/140 - 1s - loss: 0.9637 - accuracy: 0.7024 - val_loss: 0.9794 - val_accuracy: 0.7000 Epoch 14/200 140/140 - 1s - loss: 0.9373 - accuracy: 0.7110 - val_loss: 0.9505 - val_accuracy: 0.7086 Epoch 15/200 140/140 - 1s - loss: 0.9081 - accuracy: 0.7224 - val_loss: 0.9236 - val_accuracy: 0.7202 Epoch 16/200 140/140 - 1s - loss: 0.8899 - accuracy: 0.7273 - val_loss: 0.9027 - val_accuracy: 0.7278 Epoch 17/200 140/140 - 1s - loss: 0.8704 - accuracy: 0.7329 - val_loss: 0.8807 - val_accuracy: 0.7363 Epoch 18/200 140/140 - 1s - loss: 0.8510 - accuracy: 0.7389 - val_loss: 0.8703 - val_accuracy: 0.7347 Epoch 19/200 140/140 - 1s - loss: 0.8514 - accuracy: 0.7347 - val_loss: 0.8711 - val_accuracy: 0.7344 Epoch 20/200 140/140 - 1s - loss: 0.8278 - accuracy: 0.7448 - val_loss: 0.8476 - val_accuracy: 0.7417 Epoch 21/200 140/140 - 1s - loss: 0.8063 - accuracy: 0.7529 - val_loss: 0.8567 - val_accuracy: 0.7408 Epoch 22/200 140/140 - 1s - loss: 0.7890 - accuracy: 0.7595 - val_loss: 0.8588 - val_accuracy: 0.7433 Epoch 23/200 140/140 - 1s - loss: 0.7865 - accuracy: 0.7587 - val_loss: 0.8366 - val_accuracy: 0.7499 Epoch 24/200 140/140 - 1s - loss: 0.7664 - accuracy: 0.7631 - val_loss: 0.8367 - val_accuracy: 0.7438 Epoch 25/200 140/140 - 1s - loss: 0.7621 - accuracy: 0.7650 - val_loss: 0.8245 - val_accuracy: 0.7537 Epoch 26/200 140/140 - 1s - loss: 0.7508 - accuracy: 0.7689 - val_loss: 0.7958 - val_accuracy: 0.7623 Epoch 27/200 140/140 - 1s - loss: 0.7361 - accuracy: 0.7715 - val_loss: 0.7758 - val_accuracy: 0.7659 Epoch 28/200 140/140 - 1s - loss: 0.7344 - accuracy: 0.7734 - val_loss: 0.8124 - val_accuracy: 0.7534 Epoch 29/200 140/140 - 1s - loss: 0.7287 - accuracy: 0.7749 - val_loss: 0.7732 - val_accuracy: 0.7683 Epoch 30/200 140/140 - 1s - loss: 0.7056 - accuracy: 0.7798 - val_loss: 0.7689 - val_accuracy: 0.7700 Epoch 31/200 140/140 - 1s - loss: 0.6912 - accuracy: 0.7859 - val_loss: 0.7631 - val_accuracy: 0.7716 Epoch 32/200 140/140 - 1s - loss: 0.6878 - accuracy: 0.7866 - val_loss: 0.7878 - val_accuracy: 0.7644 Epoch 33/200 140/140 - 1s - loss: 0.6891 - accuracy: 0.7866 - val_loss: 0.7904 - val_accuracy: 0.7648 Epoch 34/200 140/140 - 1s - loss: 0.6817 - accuracy: 0.7891 - val_loss: 0.7935 - val_accuracy: 0.7641 Epoch 35/200 140/140 - 1s - loss: 0.6689 - accuracy: 0.7916 - val_loss: 0.7354 - val_accuracy: 0.7823 Epoch 36/200 140/140 - 1s - loss: 0.6542 - accuracy: 0.7994 - val_loss: 0.7325 - val_accuracy: 0.7835 Epoch 37/200 140/140 - 1s - loss: 0.6521 - accuracy: 0.7980 - val_loss: 0.7546 - val_accuracy: 0.7743 Epoch 38/200 140/140 - 1s - loss: 0.6540 - accuracy: 0.7969 - val_loss: 0.7449 - val_accuracy: 0.7787 Epoch 39/200 140/140 - 1s - loss: 0.6346 - accuracy: 0.8025 - val_loss: 0.7446 - val_accuracy: 0.7795 Epoch 40/200 140/140 - 1s - loss: 0.6259 - accuracy: 0.8078 - val_loss: 0.7372 - val_accuracy: 0.7838 Epoch 41/200 140/140 - 1s - loss: 0.6215 - accuracy: 0.8081 - val_loss: 0.7326 - val_accuracy: 0.7840 Epoch 42/200 140/140 - 1s - loss: 0.6136 - accuracy: 0.8112 - val_loss: 0.7062 - val_accuracy: 0.7917 Epoch 43/200 140/140 - 1s - loss: 0.6174 - accuracy: 0.8087 - val_loss: 0.7058 - val_accuracy: 0.7909 Epoch 44/200 140/140 - 1s - loss: 0.6070 - accuracy: 0.8104 - val_loss: 0.7349 - val_accuracy: 0.7850 Epoch 45/200 140/140 - 1s - loss: 0.5991 - accuracy: 0.8139 - val_loss: 0.7529 - val_accuracy: 0.7763 Epoch 46/200 140/140 - 1s - loss: 0.6089 - accuracy: 0.8103 - val_loss: 0.7171 - val_accuracy: 0.7879 Epoch 47/200 140/140 - 1s - loss: 0.5991 - accuracy: 0.8138 - val_loss: 0.7583 - val_accuracy: 0.7737 Epoch 48/200 140/140 - 1s - loss: 0.5899 - accuracy: 0.8157 - val_loss: 0.7317 - val_accuracy: 0.7853 Epoch 49/200 140/140 - 1s - loss: 0.5766 - accuracy: 0.8194 - val_loss: 0.7186 - val_accuracy: 0.7889 Epoch 50/200 140/140 - 1s - loss: 0.5763 - accuracy: 0.8186 - val_loss: 0.7177 - val_accuracy: 0.7878 Epoch 51/200 140/140 - 1s - loss: 0.5690 - accuracy: 0.8230 - val_loss: 0.7370 - val_accuracy: 0.7839 Epoch 52/200 140/140 - 1s - loss: 0.5694 - accuracy: 0.8216 - val_loss: 0.7028 - val_accuracy: 0.7951 Epoch 53/200 140/140 - 1s - loss: 0.5660 - accuracy: 0.8236 - val_loss: 0.7158 - val_accuracy: 0.7918 Epoch 54/200 140/140 - 1s - loss: 0.5570 - accuracy: 0.8260 - val_loss: 0.7537 - val_accuracy: 0.7779 Epoch 55/200 140/140 - 1s - loss: 0.5630 - accuracy: 0.8233 - val_loss: 0.6872 - val_accuracy: 0.8007 Epoch 56/200 140/140 - 1s - loss: 0.5528 - accuracy: 0.8268 - val_loss: 0.7172 - val_accuracy: 0.7881 Epoch 57/200 140/140 - 1s - loss: 0.5419 - accuracy: 0.8297 - val_loss: 0.7227 - val_accuracy: 0.7885 Epoch 58/200 140/140 - 1s - loss: 0.5353 - accuracy: 0.8305 - val_loss: 0.6836 - val_accuracy: 0.8016 Epoch 59/200 140/140 - 1s - loss: 0.5334 - accuracy: 0.8330 - val_loss: 0.6838 - val_accuracy: 0.8023 Epoch 60/200 140/140 - 1s - loss: 0.5364 - accuracy: 0.8310 - val_loss: 0.7086 - val_accuracy: 0.7931 Epoch 61/200 140/140 - 1s - loss: 0.5322 - accuracy: 0.8324 - val_loss: 0.6940 - val_accuracy: 0.7983 Epoch 62/200 140/140 - 1s - loss: 0.5251 - accuracy: 0.8371 - val_loss: 0.6903 - val_accuracy: 0.8002 Epoch 63/200 140/140 - 1s - loss: 0.5151 - accuracy: 0.8380 - val_loss: 0.6828 - val_accuracy: 0.8021 Epoch 64/200 140/140 - 1s - loss: 0.5183 - accuracy: 0.8370 - val_loss: 0.7257 - val_accuracy: 0.7897 Epoch 65/200 140/140 - 1s - loss: 0.5235 - accuracy: 0.8360 - val_loss: 0.7005 - val_accuracy: 0.7987 Epoch 66/200 140/140 - 1s - loss: 0.5155 - accuracy: 0.8383 - val_loss: 0.7209 - val_accuracy: 0.7913 Epoch 67/200 140/140 - 1s - loss: 0.5103 - accuracy: 0.8393 - val_loss: 0.6745 - val_accuracy: 0.8050 Epoch 68/200 140/140 - 1s - loss: 0.5208 - accuracy: 0.8362 - val_loss: 0.6897 - val_accuracy: 0.8056 Epoch 69/200 140/140 - 1s - loss: 0.5146 - accuracy: 0.8375 - val_loss: 0.6884 - val_accuracy: 0.7987 Epoch 70/200 140/140 - 1s - loss: 0.4981 - accuracy: 0.8428 - val_loss: 0.6707 - val_accuracy: 0.8091 Epoch 71/200 140/140 - 1s - loss: 0.5033 - accuracy: 0.8412 - val_loss: 0.6967 - val_accuracy: 0.7987 Epoch 72/200 140/140 - 1s - loss: 0.4953 - accuracy: 0.8437 - val_loss: 0.6899 - val_accuracy: 0.8047 Epoch 73/200 140/140 - 1s - loss: 0.4908 - accuracy: 0.8454 - val_loss: 0.7099 - val_accuracy: 0.7991 Epoch 74/200 140/140 - 1s - loss: 0.4964 - accuracy: 0.8434 - val_loss: 0.6768 - val_accuracy: 0.8062 Epoch 75/200 140/140 - 1s - loss: 0.5015 - accuracy: 0.8427 - val_loss: 0.6892 - val_accuracy: 0.8035 Epoch 76/200 140/140 - 1s - loss: 0.4867 - accuracy: 0.8457 - val_loss: 0.6908 - val_accuracy: 0.8003 Epoch 77/200 140/140 - 1s - loss: 0.4827 - accuracy: 0.8482 - val_loss: 0.7009 - val_accuracy: 0.7974 Epoch 78/200 140/140 - 1s - loss: 0.4811 - accuracy: 0.8491 - val_loss: 0.6796 - val_accuracy: 0.8066 Epoch 79/200 140/140 - 1s - loss: 0.4809 - accuracy: 0.8467 - val_loss: 0.7113 - val_accuracy: 0.7984 Epoch 80/200 140/140 - 1s - loss: 0.4914 - accuracy: 0.8455 - val_loss: 0.7254 - val_accuracy: 0.7938 Epoch 81/200 140/140 - 1s - loss: 0.4767 - accuracy: 0.8503 - val_loss: 0.6861 - val_accuracy: 0.8044 Epoch 82/200 140/140 - 1s - loss: 0.4779 - accuracy: 0.8483 - val_loss: 0.6685 - val_accuracy: 0.8092 Epoch 83/200 140/140 - 1s - loss: 0.4632 - accuracy: 0.8540 - val_loss: 0.6852 - val_accuracy: 0.8030 Epoch 84/200 140/140 - 1s - loss: 0.4729 - accuracy: 0.8518 - val_loss: 0.7113 - val_accuracy: 0.7969 Epoch 85/200 140/140 - 1s - loss: 0.4693 - accuracy: 0.8504 - val_loss: 0.6752 - val_accuracy: 0.8097 Epoch 86/200 140/140 - 1s - loss: 0.4635 - accuracy: 0.8535 - val_loss: 0.6717 - val_accuracy: 0.8119 Epoch 87/200 140/140 - 1s - loss: 0.4589 - accuracy: 0.8559 - val_loss: 0.6798 - val_accuracy: 0.8084 Epoch 88/200 140/140 - 1s - loss: 0.4593 - accuracy: 0.8560 - val_loss: 0.6936 - val_accuracy: 0.8013 Epoch 89/200 140/140 - 1s - loss: 0.4519 - accuracy: 0.8575 - val_loss: 0.6732 - val_accuracy: 0.8127 Epoch 90/200 140/140 - 1s - loss: 0.4484 - accuracy: 0.8582 - val_loss: 0.6841 - val_accuracy: 0.8056 Epoch 91/200 140/140 - 1s - loss: 0.4556 - accuracy: 0.8569 - val_loss: 0.6825 - val_accuracy: 0.8096 Epoch 92/200 140/140 - 1s - loss: 0.4463 - accuracy: 0.8576 - val_loss: 0.6844 - val_accuracy: 0.8051 Epoch 93/200 140/140 - 1s - loss: 0.4400 - accuracy: 0.8615 - val_loss: 0.6847 - val_accuracy: 0.8052 Epoch 94/200 140/140 - 1s - loss: 0.4371 - accuracy: 0.8627 - val_loss: 0.6837 - val_accuracy: 0.8079 Epoch 95/200 140/140 - 1s - loss: 0.4480 - accuracy: 0.8589 - val_loss: 0.6947 - val_accuracy: 0.8034 Epoch 96/200 140/140 - 1s - loss: 0.4365 - accuracy: 0.8620 - val_loss: 0.7203 - val_accuracy: 0.7963 Epoch 97/200 140/140 - 1s - loss: 0.4376 - accuracy: 0.8622 - val_loss: 0.6827 - val_accuracy: 0.8107 Epoch 98/200 140/140 - 1s - loss: 0.4353 - accuracy: 0.8611 - val_loss: 0.6841 - val_accuracy: 0.8072 Epoch 99/200 140/140 - 1s - loss: 0.4287 - accuracy: 0.8654 - val_loss: 0.6712 - val_accuracy: 0.8123 Epoch 100/200 140/140 - 1s - loss: 0.4262 - accuracy: 0.8642 - val_loss: 0.6788 - val_accuracy: 0.8111 Epoch 101/200 140/140 - 1s - loss: 0.4319 - accuracy: 0.8635 - val_loss: 0.6791 - val_accuracy: 0.8112 Epoch 102/200 140/140 - 1s - loss: 0.4245 - accuracy: 0.8653 - val_loss: 0.6750 - val_accuracy: 0.8078 Epoch 103/200 140/140 - 1s - loss: 0.4315 - accuracy: 0.8639 - val_loss: 0.6741 - val_accuracy: 0.8119 Epoch 104/200 140/140 - 1s - loss: 0.4289 - accuracy: 0.8624 - val_loss: 0.6956 - val_accuracy: 0.8078 Epoch 105/200 140/140 - 1s - loss: 0.4219 - accuracy: 0.8669 - val_loss: 0.6752 - val_accuracy: 0.8096 Epoch 106/200 140/140 - 1s - loss: 0.4224 - accuracy: 0.8654 - val_loss: 0.7135 - val_accuracy: 0.7967 Epoch 107/200 140/140 - 1s - loss: 0.4105 - accuracy: 0.8702 - val_loss: 0.6760 - val_accuracy: 0.8121 Epoch 108/200 140/140 - 1s - loss: 0.4133 - accuracy: 0.8684 - val_loss: 0.6940 - val_accuracy: 0.8096 Epoch 109/200 140/140 - 1s - loss: 0.4152 - accuracy: 0.8694 - val_loss: 0.7060 - val_accuracy: 0.8039 Epoch 110/200 140/140 - 1s - loss: 0.4079 - accuracy: 0.8711 - val_loss: 0.6600 - val_accuracy: 0.8195 Epoch 111/200 140/140 - 1s - loss: 0.4056 - accuracy: 0.8708 - val_loss: 0.7221 - val_accuracy: 0.7982 Epoch 112/200 140/140 - 1s - loss: 0.4128 - accuracy: 0.8695 - val_loss: 0.6626 - val_accuracy: 0.8189 Epoch 113/200 140/140 - 1s - loss: 0.4044 - accuracy: 0.8706 - val_loss: 0.6848 - val_accuracy: 0.8123 Epoch 114/200 140/140 - 1s - loss: 0.4044 - accuracy: 0.8717 - val_loss: 0.7006 - val_accuracy: 0.8076 Epoch 115/200 140/140 - 1s - loss: 0.4067 - accuracy: 0.8687 - val_loss: 0.6838 - val_accuracy: 0.8164 Epoch 116/200 140/140 - 1s - loss: 0.4026 - accuracy: 0.8727 - val_loss: 0.7112 - val_accuracy: 0.8027 Epoch 117/200 140/140 - 1s - loss: 0.4029 - accuracy: 0.8719 - val_loss: 0.6856 - val_accuracy: 0.8126 Epoch 118/200 140/140 - 1s - loss: 0.3879 - accuracy: 0.8768 - val_loss: 0.6912 - val_accuracy: 0.8096 Epoch 119/200 140/140 - 1s - loss: 0.3965 - accuracy: 0.8735 - val_loss: 0.7080 - val_accuracy: 0.8025 Epoch 120/200 140/140 - 1s - loss: 0.3962 - accuracy: 0.8726 - val_loss: 0.6906 - val_accuracy: 0.8119 Epoch 121/200 140/140 - 1s - loss: 0.3927 - accuracy: 0.8745 - val_loss: 0.6708 - val_accuracy: 0.8182 Epoch 122/200 140/140 - 1s - loss: 0.3884 - accuracy: 0.8759 - val_loss: 0.7086 - val_accuracy: 0.8073 Epoch 123/200 140/140 - 1s - loss: 0.3900 - accuracy: 0.8753 - val_loss: 0.7119 - val_accuracy: 0.8097 Epoch 124/200 140/140 - 1s - loss: 0.3815 - accuracy: 0.8791 - val_loss: 0.6969 - val_accuracy: 0.8123 Epoch 125/200 140/140 - 1s - loss: 0.3841 - accuracy: 0.8766 - val_loss: 0.7016 - val_accuracy: 0.8104 Epoch 126/200 140/140 - 1s - loss: 0.3865 - accuracy: 0.8780 - val_loss: 0.7037 - val_accuracy: 0.8126 Epoch 127/200 140/140 - 1s - loss: 0.3814 - accuracy: 0.8783 - val_loss: 0.7087 - val_accuracy: 0.8085 Epoch 128/200 140/140 - 1s - loss: 0.3880 - accuracy: 0.8758 - val_loss: 0.7425 - val_accuracy: 0.7987 Epoch 129/200 140/140 - 1s - loss: 0.3827 - accuracy: 0.8774 - val_loss: 0.6749 - val_accuracy: 0.8179 Epoch 130/200 140/140 - 1s - loss: 0.3779 - accuracy: 0.8780 - val_loss: 0.6948 - val_accuracy: 0.8137 Epoch 131/200 140/140 - 1s - loss: 0.3812 - accuracy: 0.8787 - val_loss: 0.6963 - val_accuracy: 0.8108 Epoch 132/200 140/140 - 1s - loss: 0.3765 - accuracy: 0.8793 - val_loss: 0.7099 - val_accuracy: 0.8118 Epoch 133/200 140/140 - 1s - loss: 0.3793 - accuracy: 0.8798 - val_loss: 0.6915 - val_accuracy: 0.8152 Epoch 134/200 140/140 - 1s - loss: 0.3720 - accuracy: 0.8806 - val_loss: 0.7146 - val_accuracy: 0.8107 Epoch 135/200 140/140 - 1s - loss: 0.3613 - accuracy: 0.8857 - val_loss: 0.7431 - val_accuracy: 0.8012 Epoch 136/200 140/140 - 1s - loss: 0.3743 - accuracy: 0.8810 - val_loss: 0.7104 - val_accuracy: 0.8066 Epoch 137/200 140/140 - 1s - loss: 0.3697 - accuracy: 0.8818 - val_loss: 0.7098 - val_accuracy: 0.8099 Epoch 138/200 140/140 - 1s - loss: 0.3716 - accuracy: 0.8809 - val_loss: 0.7418 - val_accuracy: 0.8041 Epoch 139/200 140/140 - 1s - loss: 0.3716 - accuracy: 0.8799 - val_loss: 0.7155 - val_accuracy: 0.8087 Epoch 140/200 140/140 - 1s - loss: 0.3678 - accuracy: 0.8820 - val_loss: 0.7416 - val_accuracy: 0.7994 Epoch 141/200 140/140 - 1s - loss: 0.3591 - accuracy: 0.8845 - val_loss: 0.7095 - val_accuracy: 0.8152 Epoch 142/200 140/140 - 1s - loss: 0.3641 - accuracy: 0.8823 - val_loss: 0.7431 - val_accuracy: 0.8009 Epoch 143/200 140/140 - 1s - loss: 0.3667 - accuracy: 0.8825 - val_loss: 0.7059 - val_accuracy: 0.8101 Epoch 144/200 140/140 - 1s - loss: 0.3680 - accuracy: 0.8824 - val_loss: 0.7118 - val_accuracy: 0.8128 Epoch 145/200 140/140 - 1s - loss: 0.3533 - accuracy: 0.8865 - val_loss: 0.7110 - val_accuracy: 0.8131 Epoch 146/200 140/140 - 1s - loss: 0.3566 - accuracy: 0.8846 - val_loss: 0.7222 - val_accuracy: 0.8117 Epoch 147/200 140/140 - 1s - loss: 0.3487 - accuracy: 0.8878 - val_loss: 0.7036 - val_accuracy: 0.8128 Epoch 148/200 140/140 - 1s - loss: 0.3659 - accuracy: 0.8828 - val_loss: 0.7263 - val_accuracy: 0.8089 Epoch 149/200 140/140 - 1s - loss: 0.3522 - accuracy: 0.8870 - val_loss: 0.7353 - val_accuracy: 0.8095 Epoch 150/200 140/140 - 1s - loss: 0.3624 - accuracy: 0.8830 - val_loss: 0.7330 - val_accuracy: 0.8088 Epoch 151/200 140/140 - 1s - loss: 0.3453 - accuracy: 0.8892 - val_loss: 0.7537 - val_accuracy: 0.8061 Epoch 152/200 140/140 - 1s - loss: 0.3478 - accuracy: 0.8884 - val_loss: 0.7264 - val_accuracy: 0.8101 Epoch 153/200 140/140 - 1s - loss: 0.3560 - accuracy: 0.8840 - val_loss: 0.7188 - val_accuracy: 0.8141 Epoch 154/200 140/140 - 1s - loss: 0.3460 - accuracy: 0.8898 - val_loss: 0.7383 - val_accuracy: 0.8102 Epoch 155/200 140/140 - 1s - loss: 0.3508 - accuracy: 0.8867 - val_loss: 0.7158 - val_accuracy: 0.8137 Epoch 156/200 140/140 - 1s - loss: 0.3449 - accuracy: 0.8894 - val_loss: 0.7126 - val_accuracy: 0.8158 Epoch 157/200 140/140 - 1s - loss: 0.3451 - accuracy: 0.8887 - val_loss: 0.7130 - val_accuracy: 0.8181 Epoch 158/200 140/140 - 1s - loss: 0.3479 - accuracy: 0.8881 - val_loss: 0.7152 - val_accuracy: 0.8158 Epoch 159/200 140/140 - 1s - loss: 0.3341 - accuracy: 0.8920 - val_loss: 0.7235 - val_accuracy: 0.8146 Epoch 160/200 140/140 - 1s - loss: 0.3483 - accuracy: 0.8874 - val_loss: 0.7354 - val_accuracy: 0.8103 Epoch 161/200 140/140 - 1s - loss: 0.3373 - accuracy: 0.8925 - val_loss: 0.7367 - val_accuracy: 0.8111 Epoch 162/200 140/140 - 1s - loss: 0.3445 - accuracy: 0.8893 - val_loss: 0.7352 - val_accuracy: 0.8082 Epoch 163/200 140/140 - 1s - loss: 0.3356 - accuracy: 0.8924 - val_loss: 0.7180 - val_accuracy: 0.8153 Epoch 164/200 140/140 - 1s - loss: 0.3307 - accuracy: 0.8947 - val_loss: 0.7314 - val_accuracy: 0.8148 Epoch 165/200 140/140 - 1s - loss: 0.3375 - accuracy: 0.8910 - val_loss: 0.7347 - val_accuracy: 0.8149 Epoch 166/200 140/140 - 1s - loss: 0.3352 - accuracy: 0.8914 - val_loss: 0.7227 - val_accuracy: 0.8139 Epoch 167/200 140/140 - 1s - loss: 0.3391 - accuracy: 0.8901 - val_loss: 0.7479 - val_accuracy: 0.8092 Epoch 168/200 140/140 - 1s - loss: 0.3467 - accuracy: 0.8864 - val_loss: 0.7328 - val_accuracy: 0.8130 Epoch 169/200 140/140 - 1s - loss: 0.3356 - accuracy: 0.8930 - val_loss: 0.7183 - val_accuracy: 0.8166 Epoch 170/200 140/140 - 1s - loss: 0.3366 - accuracy: 0.8912 - val_loss: 0.7396 - val_accuracy: 0.8113 Epoch 171/200 140/140 - 1s - loss: 0.3217 - accuracy: 0.8960 - val_loss: 0.7355 - val_accuracy: 0.8107 Epoch 172/200 140/140 - 1s - loss: 0.3268 - accuracy: 0.8940 - val_loss: 0.7295 - val_accuracy: 0.8177 Epoch 173/200 140/140 - 1s - loss: 0.3252 - accuracy: 0.8965 - val_loss: 0.7371 - val_accuracy: 0.8126 Epoch 174/200 140/140 - 1s - loss: 0.3378 - accuracy: 0.8901 - val_loss: 0.7323 - val_accuracy: 0.8154 Epoch 175/200 140/140 - 1s - loss: 0.3295 - accuracy: 0.8940 - val_loss: 0.7431 - val_accuracy: 0.8129 Epoch 176/200 140/140 - 1s - loss: 0.3223 - accuracy: 0.8951 - val_loss: 0.7532 - val_accuracy: 0.8123 Epoch 177/200 140/140 - 1s - loss: 0.3349 - accuracy: 0.8931 - val_loss: 0.7645 - val_accuracy: 0.8079 Epoch 178/200 140/140 - 1s - loss: 0.3227 - accuracy: 0.8973 - val_loss: 0.7397 - val_accuracy: 0.8174 Epoch 179/200 140/140 - 1s - loss: 0.3259 - accuracy: 0.8935 - val_loss: 0.7464 - val_accuracy: 0.8097 Epoch 180/200 140/140 - 1s - loss: 0.3292 - accuracy: 0.8932 - val_loss: 0.7405 - val_accuracy: 0.8194 Epoch 181/200 140/140 - 1s - loss: 0.3211 - accuracy: 0.8962 - val_loss: 0.7475 - val_accuracy: 0.8140 Epoch 182/200 140/140 - 1s - loss: 0.3226 - accuracy: 0.8950 - val_loss: 0.7502 - val_accuracy: 0.8134 Epoch 183/200 140/140 - 1s - loss: 0.3289 - accuracy: 0.8930 - val_loss: 0.7510 - val_accuracy: 0.8129 Epoch 184/200 140/140 - 1s - loss: 0.3114 - accuracy: 0.8997 - val_loss: 0.7684 - val_accuracy: 0.8122 Epoch 185/200 140/140 - 1s - loss: 0.3247 - accuracy: 0.8958 - val_loss: 0.7831 - val_accuracy: 0.8051 Epoch 186/200 140/140 - 1s - loss: 0.3224 - accuracy: 0.8944 - val_loss: 0.7760 - val_accuracy: 0.8052 Epoch 187/200 140/140 - 1s - loss: 0.3185 - accuracy: 0.8961 - val_loss: 0.7377 - val_accuracy: 0.8177 Epoch 188/200 140/140 - 1s - loss: 0.3172 - accuracy: 0.8971 - val_loss: 0.7420 - val_accuracy: 0.8149 Epoch 189/200 140/140 - 1s - loss: 0.3079 - accuracy: 0.9011 - val_loss: 0.7547 - val_accuracy: 0.8166 Epoch 190/200 140/140 - 1s - loss: 0.3280 - accuracy: 0.8921 - val_loss: 0.7568 - val_accuracy: 0.8109 Epoch 191/200 140/140 - 1s - loss: 0.3132 - accuracy: 0.8991 - val_loss: 0.7684 - val_accuracy: 0.8146 Epoch 192/200 140/140 - 1s - loss: 0.3135 - accuracy: 0.8984 - val_loss: 0.7337 - val_accuracy: 0.8192 Epoch 193/200 140/140 - 1s - loss: 0.3132 - accuracy: 0.8974 - val_loss: 0.7523 - val_accuracy: 0.8157 Epoch 194/200 140/140 - 1s - loss: 0.3086 - accuracy: 0.8999 - val_loss: 0.7454 - val_accuracy: 0.8154 Epoch 195/200 140/140 - 1s - loss: 0.3128 - accuracy: 0.8989 - val_loss: 0.7744 - val_accuracy: 0.8084 Epoch 196/200 140/140 - 1s - loss: 0.3230 - accuracy: 0.8926 - val_loss: 0.7464 - val_accuracy: 0.8179 Epoch 197/200 140/140 - 1s - loss: 0.3027 - accuracy: 0.9037 - val_loss: 0.7633 - val_accuracy: 0.8141 Epoch 198/200 140/140 - 1s - loss: 0.3107 - accuracy: 0.9008 - val_loss: 0.8033 - val_accuracy: 0.8051 Epoch 199/200 140/140 - 1s - loss: 0.3074 - accuracy: 0.9011 - val_loss: 0.7538 - val_accuracy: 0.8150 Epoch 200/200 140/140 - 1s - loss: 0.3159 - accuracy: 0.8975 - val_loss: 0.7896 - val_accuracy: 0.8078
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=1)
print("Loss:", scores[0])
print("Accuracy:", scores[1])
563/563 [==============================] - 1s 1ms/step - loss: 0.7896 - accuracy: 0.8078 Loss: 0.7895674109458923 Accuracy: 0.8077777624130249
accuracy = training_history.history['accuracy']
val_accuracy = training_history.history['val_accuracy']
loss = training_history.history['loss']
val_loss = training_history.history['val_loss']
epochs = range(len(accuracy)) # Get number of epochs
plt.plot ( epochs, accuracy, label = 'training accuracy' )
plt.plot ( epochs, val_accuracy, label = 'validation accuracy' )
plt.title ('Training and validation accuracy')
plt.legend(loc = 'lower right')
plt.figure()
plt.plot ( epochs, loss, label = 'training loss' )
plt.plot ( epochs, val_loss, label = 'validation loss' )
plt.legend(loc = 'upper right')
plt.title ('Training and validation loss' )
Text(0.5, 1.0, 'Training and validation loss')
print('Trianing accuracy',max(accuracy))
print('Validation accuracy',max(val_accuracy))
Trianing accuracy 0.9037142992019653 Validation accuracy 0.8195000290870667
There are around 10 classes in the dataset which represent digits from 0-9.
We tried training a Neural Network with dense hidden layers of different number of units and are able to achieve a final test accuracy of 80.77 %.
Also we notice that after a certain point the model begins to overfit on our dataset as is clear from the plots above where the validation loss begins to increase after certain point and validation accuracy begins to decrease.
Thus, with this amount of accuracy we are able to distinguish between the different digits in this dataset.